{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling and Simulation in Python\n",
    "\n",
    "Case Study: Predicting salmon returns\n",
    "\n",
    "This case study is based on a ModSim student project by Josh Deng and Erika Lu.\n",
    "\n",
    "Copyright 2017 Allen Downey\n",
    "\n",
    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure Jupyter so figures appear in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Configure Jupyter to display the assigned value after an assignment\n",
    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
    "\n",
    "# import functions from the modsim.py module\n",
    "from modsim import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Can we predict salmon populations?\n",
    "\n",
    "Each year the [U.S. Atlantic Salmon Assessment Committee](https://www.nefsc.noaa.gov/USASAC/Reports/USASAC2018-Report-30-2017-Activities.pdf) reports estimates of salmon populations in oceans and rivers in the northeastern United States.  The reports are useful for monitoring changes in these populations, but they generally do not include predictions.\n",
    "\n",
    "The goal of this case study is to model year-to-year changes in population, evaluate how predictable these changes are, and estimate the probability that a particular population will increase or decrease in the next 10 years.\n",
    "\n",
    "As an example, I'll use data from page 18 of the 2017 report, which provides population estimates for the Narraguagus and Sheepscot Rivers in Maine.\n",
    "\n",
    "![USASAC_Report_2017_Page18](data/USASAC_Report_2017_Page18.png)\n",
    "\n",
    "At the end of this notebook, I make some suggestions for extracting data from a PDF document automatically, but for this example I will keep it simple and type it in.\n",
    "\n",
    "Here are the population estimates for the Narraguagus River:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pops = [2749, 2845, 4247, 1843, 2562, 1774, 1201, 1284, 1287, 2339, 1177, 962, 1176, 2149, 1404, 969, 1237, 1615, 1201];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get this data into a Pandas Series, I'll also make a range of years to use as an index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(1997, 2016)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years = range(1997, 2016)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's the series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>2749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>2845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>4247.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>1843.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2562.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1774.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>1284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>962.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>2149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1615.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1201.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "1997    2749.0\n",
       "1998    2845.0\n",
       "1999    4247.0\n",
       "2000    1843.0\n",
       "2001    2562.0\n",
       "2002    1774.0\n",
       "2003    1201.0\n",
       "2004    1284.0\n",
       "2005    1287.0\n",
       "2006    2339.0\n",
       "2007    1177.0\n",
       "2008     962.0\n",
       "2009    1176.0\n",
       "2010    2149.0\n",
       "2011    1404.0\n",
       "2012     969.0\n",
       "2013    1237.0\n",
       "2014    1615.0\n",
       "2015    1201.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_series = TimeSeries(pops, index=years, dtype=np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what it looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_population(series):\n",
    "    plot(series, label='Estimated population')\n",
    "    decorate(xlabel='Year', \n",
    "             ylabel='Population estimate', \n",
    "             title='Narraguacus River',\n",
    "             ylim=[0, 5000])\n",
    "    \n",
    "plot_population(pop_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling changes\n",
    "\n",
    "To see how the population changes from year-to-year, I'll use `ediff1d` to compute the absolute difference between each year and the next.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   96.,  1402., -2404.,   719.,  -788.,  -573.,    83.,     3.,\n",
       "        1052., -1162.,  -215.,   214.,   973.,  -745.,  -435.,   268.,\n",
       "         378.,  -414.,     0.])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs_diffs = np.ediff1d(pop_series, to_end=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compute relative differences by dividing by the original series elementwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1997    0.034922\n",
       "1998    0.492794\n",
       "1999   -0.566047\n",
       "2000    0.390125\n",
       "2001   -0.307572\n",
       "2002   -0.322999\n",
       "2003    0.069109\n",
       "2004    0.002336\n",
       "2005    0.817405\n",
       "2006   -0.496794\n",
       "2007   -0.182668\n",
       "2008    0.222453\n",
       "2009    0.827381\n",
       "2010   -0.346673\n",
       "2011   -0.309829\n",
       "2012    0.276574\n",
       "2013    0.305578\n",
       "2014   -0.256347\n",
       "2015    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rel_diffs = abs_diffs / pop_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we can use the `modsim` function `compute_rel_diff`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1997    0.034922\n",
       "1998    0.492794\n",
       "1999   -0.566047\n",
       "2000    0.390125\n",
       "2001   -0.307572\n",
       "2002   -0.322999\n",
       "2003    0.069109\n",
       "2004    0.002336\n",
       "2005    0.817405\n",
       "2006   -0.496794\n",
       "2007   -0.182668\n",
       "2008    0.222453\n",
       "2009    0.827381\n",
       "2010   -0.346673\n",
       "2011   -0.309829\n",
       "2012    0.276574\n",
       "2013    0.305578\n",
       "2014   -0.256347\n",
       "2015    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rel_diffs = compute_rel_diff(pop_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These relative differences are observed annual net growth rates.  So let's drop the `0` and save them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1997    0.034922\n",
       "1998    0.492794\n",
       "1999   -0.566047\n",
       "2000    0.390125\n",
       "2001   -0.307572\n",
       "2002   -0.322999\n",
       "2003    0.069109\n",
       "2004    0.002336\n",
       "2005    0.817405\n",
       "2006   -0.496794\n",
       "2007   -0.182668\n",
       "2008    0.222453\n",
       "2009    0.827381\n",
       "2010   -0.346673\n",
       "2011   -0.309829\n",
       "2012    0.276574\n",
       "2013    0.305578\n",
       "2014   -0.256347\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rates = rel_diffs.drop(2015)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple way to model this system is to draw a random value from this series of observed rates each year.  We can use the NumPy function `choice` to make a random choice from a series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06910907577019151"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.choice(rates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation\n",
    "\n",
    "Now we can simulate the system by drawing random growth rates from the series of observed rates.\n",
    "\n",
    "I'll start the simulation in 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1201.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_0 = 2015\n",
    "p_0 = pop_series[t_0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a `System` object with variables `t_0`, `p_0`, `rates`, and `duration=10` years. \n",
    "\n",
    "The series of observed rates is one big parameter of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_0</th>\n",
       "      <td>1201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rates</th>\n",
       "      <td>1997    0.034922\n",
       "1998    0.492794\n",
       "1999   -0.56...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "t_0                                                      2015\n",
       "p_0                                                      1201\n",
       "duration                                                   10\n",
       "rates       1997    0.034922\n",
       "1998    0.492794\n",
       "1999   -0.56...\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system = System(t_0=t_0,\n",
    "                p_0=p_0,\n",
    "                duration=10,\n",
    "                rates=rates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write an update functon that takes as parameters `pop`, `t`, and `system`.\n",
    "It should choose a random growth rate, compute the change in population, and return the new population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "def update_func1(pop, t, system):\n",
    "    \"\"\"Simulate one time step.\n",
    "    \n",
    "    pop: population\n",
    "    t: time step\n",
    "    system: System object\n",
    "    \"\"\"\n",
    "    rate = np.random.choice(system.rates)\n",
    "    pop += rate * pop\n",
    "    return pop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test your update function and run it a few times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1203.806074766355"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "update_func1(p_0, t_0, system)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a version of `run_simulation` that stores the results in a `TimeSeries` and returns it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_simulation(system, update_func):\n",
    "    \"\"\"Simulate a queueing system.\n",
    "    \n",
    "    system: System object\n",
    "    update_func: function object\n",
    "    \"\"\"\n",
    "    t_0 = system.t_0\n",
    "    t_end = t_0 + system.duration\n",
    "    \n",
    "    results = TimeSeries()\n",
    "    results[t_0] = system.p_0\n",
    "    \n",
    "    for t in linrange(t_0, t_end):\n",
    "        results[t+1] = update_func(results[t], t, system)\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `run_simulation` to run generate a prediction for the next 10 years.\n",
    "\n",
    "The plot your prediction along with the original data.  Your prediction should pick up where the data leave off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "results = run_simulation(system, update_func1)\n",
    "plot(results, label='Simulation')\n",
    "plot_population(pop_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a sense of how much the results vary, we can run the model several times and plot all of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_many_simulations(system, update_func, iters):\n",
    "    \"\"\"Runs simulations and plots the results.\n",
    "    \n",
    "    system: System object\n",
    "    update_func: function object\n",
    "    iters: number of simulations to run\n",
    "    \"\"\"\n",
    "    for i in range(iters):\n",
    "        results = run_simulation(system, update_func)\n",
    "        plot(results, color='gray', linewidth=5, alpha=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot option `alpha=0.1` makes the lines semi-transparent, so they are darker where they overlap.\n",
    "\n",
    "Run `plot_many_simulations` with your update function and `iters=30`.  Also plot the original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "plot_many_simulations(system, update_func1, 30)\n",
    "plot_population(pop_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are highly variable: according to this model, the population might continue to decline over the next 10 years, or it might recover and grow rapidly!\n",
    "\n",
    "It's hard to say how seriously we should take this model.  There are many factors that influence salmon populations that are not included in the model.  For example, if the population starts to grow quickly, it might be limited by resource limits, predators, or fishing.  If the population starts to fall, humans might restrict fishing and stock the river with farmed fish.\n",
    "\n",
    "So these results should probably not be considered useful predictions.  However, there might be something useful we can do, which is to estimate the probability that the population will increase or decrease in the next 10 years.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of net changes\n",
    "\n",
    "To describe the distribution of net changes, write a function called `run_many_simulations` that runs many simulations, saves the final populations in a `ModSimSeries`, and returns the `ModSimSeries`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_many_simulations(system, update_func, iters):\n",
    "    \"\"\"Runs simulations and report final populations.\n",
    "    \n",
    "    system: System object\n",
    "    update_func: function object\n",
    "    iters: number of simulations to run\n",
    "    \n",
    "    returns: series of final populations\n",
    "    \"\"\"\n",
    "    # FILL THIS IN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "def run_many_simulations(system, update_func, iters):\n",
    "    \"\"\"Runs simulations and report final populations.\n",
    "    \n",
    "    system: System object\n",
    "    update_func: function object\n",
    "    iters: number of simulations to run\n",
    "    \n",
    "    returns: series of final populations\n",
    "    \"\"\"\n",
    "    last_pops = ModSimSeries()\n",
    "    \n",
    "    for i in range(iters):\n",
    "        results = run_simulation(system, update_func)\n",
    "        last_pops[i] = get_last_value(results)\n",
    "        \n",
    "    return last_pops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test your function by running it with `iters=5`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892.723387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.036567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>943.549004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>290.634834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003.147033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0     892.723387\n",
       "1      99.036567\n",
       "2     943.549004\n",
       "3     290.634834\n",
       "4    1003.147033\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_many_simulations(system, update_func1, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can run 1000 simulations and describe the distribution of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     1000.000000\n",
       "mean      1605.829424\n",
       "std       2799.595802\n",
       "min         13.400432\n",
       "25%        306.557442\n",
       "50%        748.584171\n",
       "75%       1686.740121\n",
       "max      47316.744869\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_pops = run_many_simulations(system, update_func1, 1000)\n",
    "last_pops.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we substract off the initial population, we get the distribution of changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     1000.000000\n",
       "mean       404.829424\n",
       "std       2799.595802\n",
       "min      -1187.599568\n",
       "25%       -894.442558\n",
       "50%       -452.415829\n",
       "75%        485.740121\n",
       "max      46115.744869\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net_changes = last_pops - p_0\n",
    "net_changes.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The median is negative, which indicates that the population decreases more often than it increases.\n",
    "\n",
    "We can be more specific by counting the number of runs where `net_changes` is positive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "348"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(net_changes > 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we can use `mean` to compute the fraction of runs where `net_changes` is positive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.348"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(net_changes > 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's the fraction where it's negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.652"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(net_changes < 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, based on observed past changes, this model predicts that the population is more likely to decrease than increase over the next 10 years, by about 2:1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A refined model\n",
    "\n",
    "There are a few ways we could improve the model.\n",
    "\n",
    "1.  It looks like there might be cyclic behavior in the past data, with a period of 4-5 years.  We could extend the model to include this effect.\n",
    "\n",
    "2.  Older data might not be as relevant for prediction as newer data, so we could give more weight to newer data.\n",
    "\n",
    "The second option is easier to implement, so let's try it.\n",
    "\n",
    "I'll use `linspace` to create an array of \"weights\" for the observed rates.  The probability that I choose each rate will be proportional to these weights.\n",
    "\n",
    "The weights have to add up to 1, so I divide through by the total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = linspace(0, 1, len(rates))\n",
    "weights /= sum(weights)\n",
    "plot(weights)\n",
    "decorate(xlabel='Index into the rates array',\n",
    "         ylabel='Weight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll add the weights to the `System` object, since they are parameters of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "system.weights = weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pass these weights as a parameter to `np.random.choice` (see the [documentation](https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.choice.html))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3466728711028385"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.choice(system.rates, p=system.weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write an update function that takes the weights into account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "def update_func2(pop, t, system):\n",
    "    \"\"\"Simulate one time step.\n",
    "    \n",
    "    pop: population\n",
    "    t: time step\n",
    "    system: System object\n",
    "    \"\"\"\n",
    "    rate = np.random.choice(system.rates, p=system.weights)\n",
    "    pop += rate * pop\n",
    "    return pop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `plot_many_simulations` to plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g/PPP57HHHgvzX21pQdAXJMnA8xMkZq1uzOa2IIHqZPxJ8Lc7gnGBmm665d4Da0FNinv1Dv780DbKlc5bAol4lGcefSDHq6XTcr5rrrmG/v5+/vCHP5DNZnn961/PT3/6U8455xzWrl3Lww8/zJe+9KXAY3/yk59w22238eijj/LqV7+a5z3vefzgBz9g/fr1vPrVr+acc87hkEMOYfXq1Vx66aWceeaZ7Nixgze84Q1861vf4qKLLuLSSy/l+uuv5wc/+AEA//RP/0QymeT2228nl8vx7ne/m6985SusXr2ab37zm/zoRz/iO9/5DosWLeIDH/hA22u7/fbb+fSnP81//Md/cMMNN/D2t7+dn//852zdupWLL76YK664gr//+7/n7rvv5h3veAdLlizhhBNOAOCHP/wh119/PX19faxevZorrriCyy67LPTnunnzZj70oQ/xta99jRNPPJH169fzmte8hl/84he85CUvafnZ7tmzhze+8Y2sXr2a//qv/2Lt2rVcfPHFDA4OctZZZwHw/e9/n+uuu44lS5bwnve8h6uvvprPfe5zoddm8RIkOpFIJLRABVlQ8XjcjU3VarWux5+EVgI13e/tj7EJnRIoa0FNgvvW7uyKOAGUKzXuW7tzUsecf/75nHjiiZ6fW25xOkT19fXx4IMP8rOf/Yx6vc73v/99zjnnnFDnfetb30pPTw9Pe9rT6Onp4TWveQ19fX0cc8wxLF26lE2bNpFKpfjud7/LmWeeSS6XY+fOnSxatCiwPmfXrl3ceeedfOxjH6Ovr4+lS5eyevVqbr75ZgB+/OMfc8EFF7By5Ur6+vp473vf23Z9J510Ei972ctIJBK86U1vIplMctddd3HbbbfxrGc9izPPPJN4PM4pp5zC2Wefzf/8z/+4x77tbW/jkEMOYeHChbzzne/ktttuC/txA7BkyRJ+9KMfceKJJzI6OsqePXtYuHDhhHVJv/zlL1myZAlvetObSCQSHHPMMbz+9a93/78AXvayl3H44YfT19fHi170IjZs2DCptVm8tLKgwnbmbmVBtUov70b8SYhEIk1r7oQF1cp66tQ1WgtqEhx35JKuWlDHHblkUsfcdNNNLWNQq1evJhqN8sUvfpFLLrmEE044gcsuu4wnPelJE553wYIF7uNYLOaZlyMZTLFYjN/85jd8/etfp1arsWrVKkZGRgK/bW3ZsgWAF7/4xe5rUvhXLBbZtWuXZ4DkRLO6Dj30UPdxJBJh2bJl7Ny5kz179nDQQQd59j344IP505/+5D5fuXKl+3jZsmWMjY25DT7DkEgkuOWWW/jud79LOp3m6KOPplgsTpgtFrS2FStWsHXr+My2Aw44wH0cj8cDv8FbwrMvFlSrFHNp9QPB9U+djj+Z79cNF1833XtgBWpSHK+WTpvLrdusXbuW1772tbznPe9h69atXH755Vx22WVcd911Ex4b5pf83nvv5XOf+xzf+c53XNF7+9vfHrjv0qVLiUaj/Pa3v3Vbo4yNjbF7925SqRRLly512/cAbN++ve17m9vr9Tpbt25l+fLl7N27l3vuucez78aNG1m8eHHgsVu2bGHBggXujB6z+HV4eDjwvW+77TZuvfVWbrnlFpYtWwbgxpvasXz5cleoW63NMr3sSwzK776TY80+eDORICEEufiCEj32haAsRuisQFkX337C9ddfzyc+8Qmy2SwHHHAA6XSawcFBwKlyHx0d3afzj46OEo1GSaVS1Go1fvKTn/Db3/7Wvcknk0my2Sy1Wo0DDzyQZz7zmXzqU58im80yNjbGmjVr+OhHPwrAK17xCv77v/+bxx57jGw2O2Hc5fe//z2//vWvKZfLfPWrXyUajXLKKadw5plncs899/DjH/+YarXKXXfdxa233srZZ5/tHvvVr36VHTt2sHv3bq655hrOPfdcAA4//HDuvfde/va3v5HL5fj617/e8rpjsRjJZJJyucwNN9yA1tpz3UGf7amnnsrQ0BDXXXcd5XKZhx56iBtuuMGzNsv00sqCCuPik6GDJnJcu/hTNwWq0y6+oG4lnXZhWoGaR7z2ta9tqoOSrLk1a9ZQq9U4/fTTOfnkkxkZGeHDH/4wAKeddhrr16/n1FNPnfJ7P/e5z+Xss8/mnHPO4ZRTTuFb3/oW5513HuvWrQOcOFE8HueEE05gZGSEK6+8krGxMV74whdyxhlnEIlEXCE699xzOf/887nwwgs544wzWLVqVdv3ftrTnsY3vvENnvWsZ3HnnXdy7bXXkk6nOfTQQ/nSl77Etddey4knnsill17KpZdeynOe8xz32GOOOYYLLriAF7/4xRxzzDG8//3vB+AFL3gBL33pS3nd617HmWeeySmnBM/APPfcczn66KN5wQtewPOe9zz++Mc/ctZZZ7nX3eqzHRwc5Nprr+UXv/gFJ598Mu985zu56KKLeM1rXjO1/wDLhOyLBdUqxXw2xJ+gO0kS3XbvAUQ6MQVxrqCUOgx4/Je//GVgnOPhhx+eUl2RpXtcffXVbTMQ23HGGWfw4Q9/mBe84AUdWFl3sb+rExNkyfb19RGJRBgbG2u6wcs2OXZsbMyzvaenh1QqRblcplAouDfweDxOOp0mmUx2ZEZSEJVKhV27djVZcYsXL54WkarVaoFjh3p7e/f5/Js2beL5z38+wOFa6yfMbdaCslgs855WPepEgIIsHfOYVinmQR0kuu3eEzqZJBFkPbXKgJxOrEBZLJZ5T6v4U9Bj/zGtevNJ3MefPCBur24KVCsh6qRAdaMA2WbxWeY073rXu6Z87B133DGNK7HMZlrFn8zHQe2O5F//8a2y9ySm1Sq21W2mw4oKGjMC3REoa0FZLJZ5z75YUEEp5mbWXNB49W6792q1WtM1BCVOTIVWs5+6cY1WoCZgf04iscwN7O/oxExkQU0kUEHHyuumddGN+U9BBFlKQcW7U2Gm3HtgBaotsVjMTiq1zHry+byd/DwBE1lQ7WqhWhWoSmxqNiRITKcFValU3LqvVrOfuvX7ZgWqDQsWLGD79u375ZgIy+ynXq+Ty+XYvHkzS5fOzQ4n3WJfLKhWwwvNMRtyvpmKP7US4MkKVC6XI5/Pk8/nyeVyM+reA5sk0ZbFixezadOmGRkwV63WqNUhHgvuFWaxgPNNdtmyZQwMDMz0UmY10x2DEovFvIHPlPUE40W5pmAGdZdoR7lc9hxfq9XI5/NNn0033ZdWoNoQjUY9zUS7xd6xIjfe/gi1Wp0TVi3llGMPmvggi8USSLs0cSHIxSfuu6DjRaBmsv9e0HpMJhuDCrKWCoWC2y9T6KZAWRffLGTDtlFqNecXa/PO5upti8USnrBdzFsV6wZNqpX9g+JP3U6QkDXtSwwqKNZUqVQC5z91U4CtQM1Cdg7n3MdjudIMrsRimftMFH9q91qlUglswup3p81k/EnW53dZTkagguqcgrpkdHL2UxBWoGYhO4fy7uNsoeJaUxaLZfJM5N5r91q1Wm3ZJXy2uPdMi06Qx2EFKsi9J6+Z27ptHVqBmmVUqzV2j4wPzKvX62QLNtXdYpkqYS2ouS5QQVmJU7WgTNdeUBunbmEFapaxe6TQZDGNWjefxTJlwlpQQaLlj8FITKpV/GmmEiQgOOmjVYKI/3i/CJuCJZ9BN8eHCFagZhmme08Yy1kLymKZKvtqQfkFyp8dJzfuVsMPO425lqDu7BMJVDv3nlCr1WYk+cMK1Cxj57AVKItlOtmXGJTfuqhWq00CJaI0Ezdw8ArwdAhUkEVVqVRm5Pq6/o5KqQXAA8DHtNbXKaWSwBeBVwFV4Cqt9eXG/ucBnwSWA78G3qi13tHYthL4GnAysAN4l9b6x928nulm51Cu6bWxvHXxWSxTZapZfLVaLbDDuV/IZtK9B/tmQQV1Kg+yqCD4M+s0M2FBfRlYYTy/FFDAk4GTgDcopV4PoJQ6GkeA3ggcAKwDbjaOvRlH7A4A3grcrJR6UofX3zFqtTq79xaaXh+1FpTFMiVa3ZzDWFB+gTJjPbMlQcJcl4kpJu0EKkiMglLOp6sz+mTpqgWllHoDMAD8xXj5DThW0RAwpJT6LHAx8A3gAuBWrfXvGsevaexzBBABTgReqLUuAXcopX4IvAX4SLeuaToZGi1QqTb/slkLymKZGmGtJ2gWKOkkIbVNQe69mY4/yToFqc8Ka0G1shD9ghSPx6nVal0X4a59okqpw4GPA282XluA47p7yNj1EeDYxuOjzW1a6xywsbH9aGCD1jrb4tg5hxl/WrJwvL2IjUFZLFMjbPxJMEXG30VCbt6zYf6TEDSnyt8lo10/Pr8FVa1WicfjTcMco9FoS9dfJ+mKQCmlYsB/A5dorbcZm/oa/5qBlxzQY2z3B2Vke7ttcxIzg++wAweINn7J8sVKoGVlsVjaMxkLCmi6sZvjNMQy8VsWMHvce0EtnFpZUEFtjMrlcpMwy7UFuf46zaQFSim1UCk12eP+GdBa6+/5Xhfrx+xG2AOMGdu9nQrHt7fbNicxBWrpoh560uMeWGtFWSyTZ18sKLlJm01jW+0/Uxl8rSyodvsIfouoXq9TLBYplUpUKhUKhQL1et29tiBB6zShP1Wl1IeADwILgSOVUh8HRoH3aa0nunu+FjhIKfWKxvN+4EvAM4FtOEkSmxvbVjHu1nuosU3W0AOsbLweAVYqpTJa63zAsXOKer3Orr2Gi29Bhv6eJGN556Mdy5dY0J+aqeVZLHOSfbGgzBiUf4wFzI74075YUH6BEkGC8U7o1WrVYx12Ow4VSqCUUh8E3g68G7i28fJ3cTLyCsAl7Y7XWq/yne8+4HONNPMx4ONKqQdw3HaXAJ9v7HoT8Dul1GnAXcDlwL1a67WN89wPfKKRPPF3wMuBU8Jc02xj71iJUtn5I8ik4vRmEvT1JGC3s91aUBbL5JmsBWW2CDJdXUG1RjMdf4JgC8ovlkGfQVCtU6FQ8FiCrZrnzsZu5hcBb9NafxOoAWitb8XJwHvdPq7hY8CDwF+Bu4FbcIQPrfVfcJIqvgzsAo4BXm0c+0rgKJwaqGuBt2itH9zH9cwIZgfzJQszRCIR+nqS7mtiSVkslvBM1oKSbeYN3LSkzDqomY4/yTpNgrqpB7U7Cuq9V6lUApMj2h3XacK6+Fbi1CD52YDj8psUWuvjjMcFYHXjJ2jfW3BEK2jbRuAlk33/2YgZf1qywMnz6Msk3NdsPz6LZfJM1YIyY07yuFUHidlkQUHr4YvmdQe59/zXkUw6X5BFnGE8DtWtnnxhLaj/xYkjCfKprAb+b1pXtJ8SlGLeb1pQ1sVnsUyKIOspKEbj3w543HqtWgnJz2wTqKD1+AcuhhWooLHxkxkjv6+EtaA+APxUKXUqkMKJ+6wCjgBe1KnF7S/U63WfBeUIlGlB2cGFFsvkmKz1BF4XnxwviQLVapVEIuHZbybFCVq7MINGhAh+N125XKZarZJKeZOw4vF44LiRbsahQllQWus/AUfiJCr8ACe9+6fAKq31Hzq3vP2DsXyZQsn5RpNKxBjodSynvp6EZx+LxRKeycafBKl1kpu6+ViONzP5ZopW2XmtXHxCkPXkPyYWixGNRl1hNulmHCpsFt/HgM9qrf/F9/qAUuoqrfX7O7G4/QWP9dRIkAAnmy8WjVCt1SmWq5TKVZKJmf3GZrHMFaZiQcG4BWK6+OQ1uZHPBguqlQBPRqDq9TqlUqnpOsRSjMfjlMveL8fdjEO1FCil1ApgsPH04zi97vb4djsOeBtgBWofMDuYS4IE4Gby7R0rAo4VtcgKlMUSiqlaUHKsmSQh7j1/ivlM1T9BawFuNTZE/jWPK5fLVCoVenq89x0RLNPl6W8D1Q1xbmdBnQR8j/GEiN+02O9r07qi/RAzQWLxgrRnW18m4QrUaK7EogHvdovFEsxEFlStVnPjL6Y7L5/Pk81m3cLVQqFAMpkklUp5BGomJsyatBLgoDXJ9bVKjjCPicfjTdN5/QLVrThUS4HSWn9fKXUYTpzqbzhdH3Yau9SBMa2136qyTBKvi8/bSrDfjEPZTD6LJTTtLCgRoqB9zCJd0+Lw1wjNdILEZCyoIIGqVquUy+UmK1Dce0IsFms7Er6TtKywifwAACAASURBVI1Baa03NB62tGOVUsnGuAvLFMjmy2QLjvAkYlEW9HkzaXozSc++FoslHO0sqEql0jJd2t85wn+sCNNM9d8TJitQ/uGEYj2m0+NemVgsRjKZ9AhZPB6nWCx6ztetOFTYJImlODOWjgbka0MEJ+X8aGBBR1a3H7DL497LEI16/8NNC8oW61os4QjqngBegZroePNf81hpJzTTFtRkXXz+5IhisdjUGikejzcJlL9A2Xz/Tn8GYSN8XwXOAR4GnoszcDCHM2r90s4sbf+g1QwowSzWtZN1LZZwtLMuggpVWx1vioDEapLJJJlM899qt5msBWVec6VScQcwmqTT6UDRCSrY7cZ8qLACdRrwBq31u3H65t2gtX4pTvPW53VobfsFrTL4BG8tlLWgLJYwtIs/tYqf9PT00NvbSyaTIZVKkclkSKfTZDIZenp66O/vp7e3l97e3hnN3hPaWVBBImWmi1cqlaZEh3g87saf/CI1U3GosJ9yCnis8fhh4BmNx9fhdBG3TJGJLKjejDdJotvzWCyWuchk3XvxeNwtTpV9g6wGmNnUcqHVfcB0Q5qYHSFqtRqlUol6ve4RokQi4cbV/NcoXSX85+z0/SjsJ61xXHvgCJSMtOijeWigJSSFYoWRrGMVxaIRFgakkKcSMRJx57+pUq1RLHV/qqXFMtdoJyxBAiWWg8Su/N0kYrGY+3gmU8uFiZrE+tdYqVQ8mXzSvslMmzfT6P0WVLs4VCcJm4byaeDrjdHt3wIeUEpFcFLPf92pxc13TOvpgMEMsWjzL34kEqG/J8mekQLgxKHSqZnNHrJYZjutLKhW3/rlhiwdJEzrSbozSEfz2SBQ7bIM/Y8BT7xJinPNdPJ4PO7JSgyb/NDpeqiwvfhuxIlD/aUxLPAsnPHqvwbe0rHVzXMmcu8JnqaxNg5lsUxIKwvK37YHvIWppkCZFpMI1mxw78HERcj+gmSp55LYkznKHZoFKug6g5rQdjoOFfqruNb6LuPxL4BfdGRF+xFBHcyD6LPFuhbLpGh1A28VfxKCBEqsJrGgZgMTxcb8XR9gPJNPrCnTnWe2cRL8jWIlDmWeu9P1UGHroA7FSSc/BidhwoPW+mnTvK79Au8U3eYMPsE7WddaUBbLRLTqEBEkXKZAmTEoOYfUCs0mgZqMBSUiIz0FJf4kmMkRJtFo1CNQ0Wg0UOA7WQ8V1oK6CVgE3AwUOrKS/YxSucrwqFOdHY1EOGCwdY+9voy1oCyWsLTKLAtyR/n70LVz8c0mgZrIgjIHL8p1i3uzVqu503KlMWyQQMVisSaXaJCl1Mk4VFiBOg74O631/R1ZxX6I2UFi4UCaeKz1L74t1rVYwtPq5j2Rew+8Lqv5YEH5Wxv5pwBL7CnousKKTifjUGE/7fuB5R1bxX5I2PgT2GJdi2UyBN28W/XX87v3ZB/zHGa8ZjZk8EH4NHMR5cm69/znE1oV7HaqHiqsBfUW4AdKqW8CjwOeFWqtvzHdC5vveONPEwhUxoxBlbs2LMximYsECVFQWx+zMBeCRcy0NsTNN9N/exP1GZTHpntPMvfq9bqnW0Q0Gm3b9NafKCGWqP+z7FQcKqxAvR54CvABIO/bVgesQE0S/xTddiTiUdLJOIVShVqtTq5Q8XSYsFgs47QqKA3qjuDfx7SgxLVnFrPOFoHy41+TZCyKKJnuTdO912oCr+BPlAh6L+hcHCqsQL0deJvW+j+nfQX7IZVqjaFGgkQkEmHx4MTNOPp6EhRKzi/ZWL5sBcpiaYHfCvJPkRXaCZQImtnXrlU3hW4Ttv2SiJLpgjNrvvy1T0EEJUoE0ak4VNgYVBG4syMr2A/ZvbdArfELM9iXJBlijHt/xo7dsFjC4BeRarXa9K0/yHIwhcyfwSePO93aJwxhLCh/53JB3HsiVP7hhH6CrKIgMexUHCqsQH0S+IRS6oBpX8F+yEQdzIPoNTL5sjaTz2JpSdBYiInce+CN7ZgWlBzbaQuqlaUXtJ8f//WVSiVXUMW6MWNuYdx7QeeF8fhWmHXtK2FdfBcAxwKvVEqNAJ47pNZ66XQvbD4TtsWRiVkLNWoz+SyWlpg3T0kU8FsYQZaDdPw2i3X9AtWJm3CtVnPHz0ciEdLpdFvX20QWlGTr+TtnmJ3Kw7j3BH+iRCs6EYcKK1BfnNZ33c+ZTIq50G/bHVksExJkPfnnI7WyHMyOCzCewWeKVCcsqGKx6EnOKBQK9Pb2tkzGmEigSqXxL7AiUGa2nv/fiQgSqKC1dSIOFWqFWuvrp/2d91Oq1Rq7905eoLztjqxAWSxB+G/eIlAmQTdmf4IEeFPMOyVQtVqtqYBYrL5WAtLOxWeez58cIUj8Kay1E7Y4uasuPqXUt4GLtNYjjcct0VqfN+0rm6fsGSlSrTm/NAO9ydCjM7ztjqyLz2IJwrxJyo3efyNuFX/yF+n6U8z9558OWo1NbydQ7SyoIOspEom455I41ETJESathKxVLGo6aXd3zOLUOMljyzTgKdANaT2BI1DyC5EtVKjW6oHzoyyW/RnzhmneoIVWlkOQBWX26TN7200nrVK4W7nLWhXpSgKHnM8cUGimlksPvrDuPTl3EIlEwiOIcu7ppOUqtdZvMp5+HNiktfZ8fWgMMDxu2lc1j/EW6IbL4AOIxaJkUnFyBaeTRDZfZqB3+n8hLJa5jGnhmPEXoZ3bzMzgk+PMBAlhuop1zTHsQduC3qed9WSKnWTxAZ7OESK6k01mCIpDRaNRenp6XCu1E4W6YdPMHweCUsxXAr+dvuXMfzwZfJOwoMCXKGEz+SyWJsw6Jrmhmjf5iQTKtFCCYlCy73QwUQFskBXVznoSa6ZSqbgZgab1ZNZATZZWVmcsFiOZTHa/m7lS6s3A2xpPI8DtSim/w/RA4Imwb6aUOgunpupwYAfwaa31V5RSSZxMwVcBVeAqrfXlxnHnNY5bjjPF941a6x2NbSuBrwEnN875Lq31j8OuqZvUanV2TyHFXOjLJNjeeGwz+SyWZkQ8/P3jwBuLCTrOX4ckAuW3ZKbDzedvPxREUBwqSLTMtkYwLnyRSMR1uwVl8U2GdlmPnaTdSr8FHIwjTicCvwTGjO31xvPvhnkjpdTyxr7naq1/opR6BvB7pdTdwKsBBTwZGAR+qpTarLX+hlLqaBwBeglwD3AFzlyqMxqnvhm4C3gp8Bzg+0qp47TWfwuzrm4yPFakXHX+gPoyCXrSk2tX5MnkswJlsTQhN2nz5m+29vEjrjSz67cg7rAwbrbJEqbzgl8ATCvJJBqNuq9LDRSMWz3VajVQqCZDkIU0owKltc4ClwEopZ4AbtZaF6f6RlrrrUqpJVrrUaVUFMdlWAFGgTfgWEVDwJBS6rPAxThNaC8AbtVa/66xljWNfY5gXDxfqLUuAXcopX6I0339I1Nda6fwdpCYnPUEvkw+6+KzWDyYLjrz5tlKoAqFAuWyE9MdHR1tamXUak7SdLj4wva3M603Wasfc93+pIVIJEKxWHSvJZ1uPRi1HebYe5OgJrzTSdgz3wCcr5Q6BEAp9f+UUg8qpf5LKdUf9s0a4tSD09vvZ8A1wE4c191Dxq6P4HSuADja3Ka1zgEbG9uPBjY0xDTo2FmFGX9aPBWB6jH78VkLymIxkZunaZ2YFpBpBdRqNc+E2Wq16hEA0y3ovwHvqwUVxr1nrlOOCbKe4vF400h3GE+IiEajLFy4kL6+Pnp7e/cpVjQTbr6wAnU58FlgqVLqdOBfgR8ATwX+fZLvWQB6gZOANwPvabyeM/bJAZLi1ufbZm5vt23WMdUMPqHfU6xrLSiLxaRd/MlMFgCvC9A8zj9Ft1VT2X0hSJxaxcdk31bWUyKRcPcxBUzO5U+U2Bdmws0XVqAuAF6ttf5f4HXAb7TWH8FJojhnMm+ota5prUta63uA/8Rx0QGYJkUP4/GurG+bub3dtllFvV6fUg8+ExuDslhaExR/EoJGa5jHSYKEmZrubxbrf5+J1lIulykWi037B7n34vF4SwFoZz2ZsTOz7ksy9sTNNx10yt3Z9j1D7rcQWNd4fCYgWXJ7gVDFOEqpU5VS/+t7OQUMAdtwkiSEVYy79R4ytzVchCsbrz8ErFRKZVocO2sYyZYolZ1vG+lk3BNPCktPKk608cuWL1aoVGe+9b/FMlsQF5d/XDs0C5T5zV/EyXSRybFmNwlhIoGq1+uMjIywa9cu9uzZw65duwLdcCatRq9Xq1VKpVJL60nEzhQ9Eacw4zQmw0xYUGHTOf4CvF4ptQ04CGf8ewJnwu59Ic9xH7BCKfV+4PPAs3CSGc7FEaiPK6UewHHbXdLYB+Am4HdKqdNwsvUuB+7VWq8FUErdjzMKZA3wd8DLgVNCrqlr+CfoTuVbTTQaoTeTcOdBjeXKLOhPTdsaLZa5TFBsJ8jF5R/tblpQ1WqVRCLhipMZ4zH3b1esWywWyeXGIw+VSoWxsTEGBwcDradoNNo0Vt58r0KhEDguRNZldpAwRSmRSEzr9N+ZSJQIe9ZLgPcBXwU+qbV+FEdAXoUjUhOitd6LY329AtiD4967SGv9a+BjwIPAX4G7gVuALzeO+wtOrOrLwC7gGJy0dOGVwFE4NVDXAm/RWj8Y8rq6QrVWZ+OOUff5VDL4hD47uNBiCSSo8WpQWnVQx3MYFyr/LKigm3wrK6perzM21hxhKBQKnvcyMa0cv5VSqVRaHhNkPQW1NZpOup0oEbab+W+VUkuBwUYqODiWzPu11oWwb6a1/j+cWiX/6wVgdeMn6LhbcEQraNtGnBqpWUW9XmfrrixrNw7z6MZhd1w7TC3+JPT1JGG3k7SYtV3NLRaXoESCoOQD84ZqpqSbvfjMDhJBVkMrgSoWi4FWkjnzyY+5vlgs5hHMUqkUaD2ZnS+C3HvTbT2Z6/MLklidnWAyFVu9wOuUUkcC/4aTyv0wThskC84v1O69BdZuGGLthqHAsRiJeJQVS/qm/B59nnZHVqAsFgi2niD4Rm2KhNkk1j9NF8bjUH5hMUVMqNfrZLOt+2qPjo6SyXi/nJpTbuW5IN0h/PVQyWTSY5HJus1zdcJ6gu4nSoQSqIYo/QoYAZ4EfAEnm+/lSqkXa63/0LEVzgH2jhVZt3GYtRuG2DMSbFD2ZRIcsXIhT33SAZPuIGHS32NdfBaLH7ODghCLxVpOzgXH4spms2SzWY8lZYrBZFx8pVKpZQFuvV4nl8uRTqc95/OvT7pX+C0jEURpvSQWVFA3cYmhdYJuJ0qEtaA+D3xba/1epdQogNb6QqXUNcBngGd3aoGzmfVbR/jzQ9vYvsdfiuWQTsZ5ysGDHLlyIcsXt56QORn6MjbV3GLxEyRQQe498+Yu4pTL5VxRkrRss9A36GYf5PKT87RbX7lc9lg3QZl7sVisyR0oHcNTqRTFotPQx289iXh0ynqC1i7PoLlb00FYgTqF8YJak6uA+6dvOXOHXKHMj37/eNN/VCIW5fAVjigdsrSPWGx6v8nYwYUWSzOtaotaFdmWy2U3fVuy/6Q5q9yA5WYc9MXS79YqlUqBtUqCiEmpVPLMZAo6dywWa7oeUwBMC1AwO5V3svUQOCLl/zIQ5PKcDsIKVB5YCqz1vX4EjttvvyMaiZBOxsgXK0QjEQ49sJ8jVi7k8IMGSMQ703oebAzKYgkiSKCCLAm5sUoBrdnBXGqO0um0Jw41kYtPrKdWsRjTfVgqldxzt0osMONi5rpTqVTTSA3wZip20noSupkoEVagrgeuUUrJ+I0lSqmn4rj+bpz2Vc0B0qk4rzrjCIZHiyxb1BN6dPu+kknFiceiVKo1iuUqpXKVZKJzgmixzHbCuvdkXxhP9xarSZ6bnR/8fflMTAFplbmXyWQoFouuS07er1wuk06nW3YVr1arTW60WCzmSQQJsp46NTTQT6vZUJ0g7F31ozjjNe7A6f5wF04n8i8BH+7IyuYAg30pBvu6WygbiUToyyQYHnN+6cfyZRZZgbLsxwRZHGbxq39f/3gN/zBC/+iNdhZUrVajWCw2CWQikaCnp8d1HfrP38raEMvI70ZLJBLk83l39pM5jLGb1hO0roWarknDJmHroCrAGqXUpTgzm+LAo74u4pYu0dczLlCjuRKLBqbWQt9imQ+Y7i4hSKDMdkZmHZGZqScCYJ6z1U1XYkpBsadUKkUymSSRSDSJV7lcbtuFArxuNLGMCoUCmUymyXqSRI6pzHmaCu06Sky3BTepK2oU1P51WldgmTTeRAkbh7Ls3wQJRFD9kz+5IMgtJTfeQqHA4OCgaxUE1UJJooW//iqRSJBKjXtW/DEbf6slwRRF80afSCTc2JW/l5/ZFLabdCtRorPpHpaO4O1qbjP5LPsvrWYrBd2w/UP9zFiT2Rg2Eom4NU1md3M/Y2NjTbEnSVUX4ajX6003bf+IdsGMVckxYj3J/uY+pvXUqU4OrehWPZQVqDmId7Lu5Cyoer3On/+6jZ//ab3NArTMeYK6g0cikbY3ULOVkOna83cvz+VyrgD5BUBiT6ZgyH5SS1WtVgNdb2J9mMIa5KaUQmOzhZN5jNnWqNtYgbK0xBxcONnJuus2DvPnh7ahNwzx+/u3TPfSLJauYg4ZFNolSJiCZiY6pFIpjzhFo1FKpRL5fJ56vd5UsySZe6YFJZ3ExXqTWFM8HndFStoR+ZvAtnJTxuNxz3vIfma9Uzcy9/x0q+VR6BhUo1ns04AE4HHuaq1/HHiQpSN4a6Em5+J76PE97uPHt+y1aeqWOU1Qg9ig4X8S9zH3r9VqrksvnU678SQzCWBsbIyBgQFXeCRjT35qtZrrakskEm6bIbNVkemmM4ttzV57QdZHT0+P24ZJ1iv7mS7MThfmBhGUKNGJ5rRhe/G9BSelPMiWrAP2DtdF/JN1w6Z3jmRLbN45PgqgUq3x+Ja9qEMXdWSdFkunaWV5+DELZQURKLG4MpmM24RV/p5kltPChQtJJBIUi0VKpZJHVCqViht3kvc2raNYLEY8HqdUKrmWlKRlm3Euk2g0SjKZZHh42LNeM2lDzt0JYQhDKpVyPy/oTKJGWAvqgzizoNZorUcn2tnSWVKJGMlEjFK56hTslqqhCoUfWb+n6dvmuo3DVqAscxKzQ4NJkECJ+8l07/lv9nKDFeGS/UZGRhgcHKRSqVAoFMjn8x5RqVarJJNJV4igWaDESjPFpFqtks/nAy2gVCrlFuya+4PXYpoJ954g1qLUbXViLWFtw0OAz1txmj14BxdOHIeq1+s88sSeptc3bBulUGz+BmexzHaCXGOtbpSmxQLjCRK1Ws0THxocHHTPI5TLZYaHh924U7FYJJ8fn5AtN2hT4PxWUSwWI5321itWq9XAIl9JrCiVSp5rCUrjnkmBkvdPJBIdW0dYgfoZ8PyOrMAyJSYbh9q8c4yRrLNfKhlzp/rW6nUe27y3M4u0WDpI2AJdwBMzkudinYhARaNRenp66OnpaTp+z549FItFV9T88SBpmQTBbsd4PO5aWYKIk1/M5Hxmg1iJocmwQmGmBarThHXx3Q9cpZR6GU7DWM//gNb6Q9O9MEt7Jjt242EjOUKtXMhgb4qdw5sBWLdxiGOedMD0L9Ji6SBBsZugbt7+ybOSLCEJEmbmXjQaZWBggNHRUU+dVKlUYmRkxGMFScsiM41czu0nnU67SRJmPCyVSrkxLLOmSSw0uRYzZd0Uq5mKP3WLsAJ1KvAnIAM83bcteACKpaP0TWJwYbFc9VhJqw5bRE86we8e2EK9Xmfzzixj+bLHbWixzHammiAhN3vTvSfHRqNRent7PTVO0uBVYlNitZjHJxKJpr57Jul0mlwu53aFkH3FkhMLLJVKeVyE8n5mggU44mh2q5ivhO3Fd3qnF2KZHP2mBTVBwe2jG4epVB23wOIFGZYsyBCJRFixpJdNO8ao1+s8tnGYpx+5pKNr7ibFcpUtO8dYfkBv1zrNW7pHUIEutE+QEJdguVz2dGqQ48SCSSaT9Pb2uvOiRDDEkpJkCBEssWTMTugmYh1JR3IZSAjj3dRlkGE8HvdkxskapVZL6NSAwNnGZOqglgHvBI7BiV09DHxVa/23Dq3N0gZPDGoCF9/DRnLEUYcuct0CRxyykE07nLTztRuH5pVA3fa7x9mya4wlCzKc94Ij570rZH+j1Yj3dgkSQfEecaGZY9IlFpXP58nn827sSlogpdNpT22TiGI+nyeZTHpcjNL6CHCFSOJYsjb5N5lMNo16l/X408k7NSBwthEqSUIp9Uyc2NO5wC5gJ3AW8IBS6sTOLc/SirBJEntGCmzb7TSdj0YjHHnoQnfbk1cMEm380m/fk2PvWDHwHHONkWyJLbsc4d05nGdodH5cl2UcER2Tdi2OyuWyR6DMFHOxnsxYVDwep6enx3W/icDJv/F43BUmEZxKpdLkdjSb1ppFumYau2QBioUWRLc6lc82wmbxXQl8EzhWa/2PWuu3aq2PBa4DPtOpxVla0+dz8QW5FgBPavnhywfIGO6udCrOygP73efrNg4zH9i0w1sNsXWXnQoz3wjbwVwSJIK6jotVYg78kw4J4sZLJBKutWN2oxARk2GHZnxL/hZN60mey3pMIRXryd86STBbJQnSLmm+E1agTgT+XWvtvwteDZw0vUuyhCERj5JOOr+0tVqdXKH5l7VWq/PI+iH3+VGHN2fqHXHIAvfxug1DTdvnIuK2FLbuGmuxp2WuMtkR76ZAicUjImNaOOaYd0laMJFuDtLiKB6PUywWPZaZPJbYU9B6TMGRXn/m+HkTyS40CRq7Ph8JK1BbgcMCXn8SYIt3Z4j+nvZdzddvGyFXcF7vTSdYuay/aZ8nrRgkHnN+DXaPFNi9N9+0z1yiXq83CdQWa0HNK1qNeG+XwZfL5dzXRATM1kRiNZlCICnd5nnlfH6BMmuTKpVK4HrkdfCKYSwWa+kBSSQSgSM7rEB5uQH4T6XUuUqp5Y2fVwBfbmyzzADebhLNLg/TvacOXUg02pwokIjHOGz5gPt87Ya57eYbGi26oiyMZEt2tMgcpVarkc/nyeVyrhstKIOvXQdz6QBh4m89ZCZImOcsl8ukUinX4jH798m//uQLybDzW0/FYtHzuiRatBMbEUn/tUnMqhMdxGcTYQXqEzjdJL4NbAI248SkvgN8pDNLs0yE2TQ268vkyxXKPL5lxH1+1GGt++153Hwbh1p+m5sL+ONPgnXzzT3q9brb907aAskgQT9+60eoVqse6wnG3XfmTd/sJmG+vwwzzGQy7nskk0m3V57UVJkCFZQxGjQt1yzyDRIoESc5p5ka364L+nwibB1UCXirUuoSQAF54FGt9dz2B81xPMW6vky+dRuGqTV+uQ88oJeFA94+YCaHLh9wm8+OZEvsGMqzbFFzu5e5gOne600nyDasqa27shxxyMJWh80asvkyW3aNcciyfjfGuL8SNOspKNkB8HQRl5qlWq3G6OgoIyMjHusLoL+/3/NFTG7+pkD52yFJnVJfX587L0rETjqcx2IxksmkuwY53j8tt1KpuMkPrYRGEicEsdYkI1HWuGjRokBRnA+0/AtQSp0J/FxrXW489nOIUgqw86BmilYNY+v1Og+ZtU9trCeAeCzKk1cMuvVSazcMzUmBqtXqnnEiz1i1lN/e57RzmguZfNVqjVvuXMdItsSKJX2ce9pTZnpJM0qQENVqtaZCVsAVBbOJq3QfF/GA8fHu/uw6c9y7IDVLMJ6oYLoCC4WCm6YuwmlaRVJ8K10oBNOCEkvIHKUh+8RiMc+1mgW+so5yuey2TJqPtHPx/QhYaDxu9XNrJxdoaU2/Zy7UuAW1cyjvJjvEY1GPC68V5j6PbhymVpt7br5dw3mKJedG1JtOcNRh498sd+0tUCrPbnfI1t1Zt6Hv5p1j7Brevx0UQVaFxJRMxP3lFy4RjaBmrOYNPch6koLZoJiRtEkyWxWJJWRmBYr140+JN1PUzf3NdUrRrilsYrWZrsl6vd6yg8V8oKUFpbWOBj22zB56DQsqayQBmJ0jnnLwYKiJuQcv7SeTipMvVsgWHDfTwUubs/5mM6Z77+ClfSQTMRYPptk57Izt3ro7y6EHDrQ5w8yyYZs3fqY3DLG40XV+f0M6hvupVCqUy2VPU1iJAZnNXaX1kPk6jMefgmYq+WuTRCDNjhFmx3Kzs7nZ+si8Bn+Gn5DJZNx1igVWrVbd0RVSX2XiF0zzMzE7WswnwnaSuEMp1fQ1XCm1RCn1v9O/LEsY+jLjhYnZQoVqrU6lWmPtxvF6plUTuPeEaDTCUw42kyXmXjafmSAh4rp8ca/72mx3823Y7hOo9UNz0pKdDloF/+Wm7R8IKEKUz+fJZrPs3buXQqHg2U9Sxv1dwEUQTLefKVAiZlK4K4IggiaPpWO5SVBBsX82VDKZdN87kUiQyWSaPgMpEA4SoVYFvvOBdjGo04CjG09PBS5WSvlTpI4Cnhz2zZRSLwQ+BRwB7AA+o7X+ilIqCXwReBVQBa7SWl9uHHce8ElgOfBr4I1a6x2NbSuBrwEnN875rv0lJhaLRelJxckWnE4S2XyZ7XuyrptroDfJiiV9oc93xMoF/OWxXQA8tmkvzztuBbHY3DCeq9Wap95pxVLnupcv7uWBR51rms0CNZYvN7n0coUyG3eMzmqrr1O06pIgN2KxGMy+dubwv0Kh4MabxJ1Xr9fp6emht7fXnfkUjUbp6+trskrMuJE5PDCZTHpGccB4d3QZCW/GkoLwz4WS1+RfOdYUKHFrJhKJJvEWSy2VSs27/nzt0oR2A5cAkcbPahzxEOrAGPCBMG+klDoEuAV4A/AD4ATgdqXUE8BpONmBTwYGgZ8qpTZrrb+hlDoaR4BeAtwDXAHcDJzROPXNwF3AS4HnAN9XSh23vzSx7esZz1Qby5c87r1Vh00uu2f5Ab30ZRKM5csUShU27hjzMP0+FQAAIABJREFU1EjNZrbvybkd2wf7Ugz0On/wyxf3efapVmuzUnQ3bgtOj9frh/ZLgQqyoMwuDeZjuWmb1pVYPZLIAOMxpHQ67WkSG9QeybRIzNZHpiCaU3llu5ksEYQ5Fj6o/klmQ5nj7M0xHqlUikKhEJjdaHZpny+0i0H9BadTBEqpO4FXaK33pRfOYcBNWuv/aTy/Wyn1K+DZOKL1xsb5h5RSnwUuBr4BXADcqrX+XWMtaxr7HIEjnCcCL2ykwt+hlPoh8Bb2k/qsvp4k2/c4dR7bdufYuN2Jw0QiEVYdGs69J0QiEY5YuZB7HeOUdRuG5oxA+eNPQl8mwUBvkpFsiUq1xs7hPAce0Bt0ihllw/bxmrUjDlnIuoab9m+b91IqV0PFEecLQY1g5fWgjg0iDObrsVjMk+FmFvKarrygJqxBXc+l64SkkgdNvBUhaydQ/vduJVCmCIr1ZI4D8SeESEFzKpWaVynnob5Kaq1PDxInpVRSKXVKyHP8Vmv9NuPYRcBzgXtxXHcPGbs/AhzbeHy0uU1rnQM2NrYfDWzQWmdbHDvvMVPN71u70/3DXrGkz7UiJoOZzfe3LXspV+ZGpbo3/uR1ax5kxKFmY9ujWq3uiT+dsGopBww6cYhKtcZjm/a2OnReEuTei0QiTQkPcuM2O0tIAoV0F5ckBkkuCIo/+QmqvzKz6vyiZgqNjGsPSowwrad2721aT+XyeCNoMxYmndZNWhUxz2VCVQIqpU4GvoIjCH5Rq4c9j3G+QeCHOFN6JcnCLPfOAVKI0+fb5t/ebtu8x+zHZ7b4OfrwyVlPwpIFGRb0pxgeLVKu1Fi/dYSnhEhTn0nKlSrbdo//GvjjbssX97lNc7fuyjrO5FnEjqGcGzfsSSc4YDCNOnQhf3jAiUk9sn4PR03x/3Mu0qpodWxs3Eo2BUisJ7E25DWz9klqnEyBkNonP2bXB8EUExE7s6efuBylQ4VYQib+WiXzPOY4j0Kh4Fp/uVzOfZ9UKuXGwuQ9zXhXrVYjl8sFNs2dq4R1xn8eZwbUa3G6SFwArMGJQb1uMm+olDoS+COwHScpQr46mvm0PY1zA2R928zt7bbtF5hjN4RUIsaTVgxO6XyRSIQjjY4L6zbO/g7nW3Zl3a4ZBwxm6El73Sv+TL7ZVjNippcfemC/83+wcqF749m8c8ytj5rvmNaDid9qEcvCtFZGR0fJZrNu7769e/e6N3CpV5rIvWe61EzM5q/iaoNxcYJxyy8oRbzVMEVxF+ZyOQqFAsVikWw2Sy6XY3R01LWKyuWyO9ZD4l6RSIR8Pu8RU7Oz+nwgrEA9DXi/1voWHJfcdq31p4H3AO8N+2ZKqefhWE3fB16ltS40XIfb8H6vXcW4W+8hc5tSqgdY2Xj9IWClUirT4th5j9nuSDjikAVuh/KpYLr5ntg6QnGWF7i2ij8JC/tTbtugQqky6wYYrt82Hn+S+Vx9mQSHLBu/lrXzZBTKRATdXM0RGDCeAm6OYxdrQ/Y1Z0BJnZTfgopEIhQKBfL5vOsaC2o7JG5C83ixlEzRERekJF7IeaQxbRCxWMy1+syR9GZihPk5mJN/JbZmuvXEipovhL2LVQD5K1oLHNd4fCfOCPgJUUo9GafzxMe01mt8s6VuAD6ulFqslDoMJ3tQuqTfBLxcKXWaUioFXA7cq7Veq7XWwP3AJ5RSKaXU6cDLG8fsF5gNY4WguU+TYeFAmiWNAtFqrc7fZnkMpF38CZwbxGythyoUK+wYclx5kUiEQ4ziaDPJ5ZH1e2ad5dcJgqwnsTIklmT21ZMb9NDQkMdlJ62DpGbJLzAiYCIG4lbzx4/kHGZ7IlmTaY3J/42sX8bCp1Ipenp6AhvZyv7lcpl8Pk+xWHTruPxWnNmoVpB5Vf7YXC6XmzdNZMMK1J+AdyilojiC8JLG608FwvoeVgP9wOVKqTHj5wrgY8CDwF+Bu3HS0b8MbjbhmxvPd+EI4quN874Spx5rB3At8Bat9YMh1zTn6UnF3bHtAIsG0ixduO/dB8zGqus2zd5v74VihV3DTkZTNBJpWfflFajZ4wHeuGO8pmbZoh7SxsTjww8aJBF3/kSHR4uukM1ngiyoeDzusRLEcpI6p6GhIU/LIbMZayQSIZfLuWLmH7VeKpXc+ikZy+F3mZkZgGI1JRKJQIGSdUrMy7R4/Ii1ZCZCAK67z0TEyR8zy2QyRCKRps/H7Ek4GSTF3r+mmSJscsMa4Mc4IvA14J+UUn8DlgFfDXMCrfX7gfe32WV14yfo2FtwRCto20bGBXO/IxqN0NeTcGMUk619asURKxfwh79sAWDT9jFyhXJTbGc2sHnnmPuHtGRhpmU69mzN5DPjT+LeExLxKE85eIFb2/bIE3vmZBPfsLRKL5eUccnWi0ajFItFEokE2WzWTSUXgRJ3ntlQNSjeVCgUPNlyYo0Ikmhhzo0C3Pcy09El2UGuwXwe9N7y/qVSye1SDuMWW61W82TqZTIZent73c/JnMwryRMm2Wy2reUWRL1ed8Vcrqm3t3dG09bDppnfjVPHdH0jZnQCcCWOZfO+jq3OEgpxa6WTcVYdOj0jJfp7kixv1AvV6vVZm+rsjT+17h24ZEHGjcvNlgGG9Xqd9aZABUw8NltVrds4TLU6N9L+p0Ir914kEmlKoZabaC6Xc1PQzflJZs86cYGVSiWP68//fvl83rU8pOeffzJuJBKhv7+/aVy8GXMyRa5V2rdYKeVyualJrfQKrNVqbmPbRYsWkUqlSKVSTfGsnp4eN4Zlfj6TsaLE6vK7N4MSRrpJ6PTwRq1RtvF4G3BNpxZlmRzPO/5gVi4bYPGC5gy2feGIlQvYutuxNtZtHOLYpyyetnNPFxMlSAixWJRli3rccRxbd43N+Hyo3XsLbmlAOhln6cJm6+igxb309yQZzZUolCqs3zY65QzN2U4r9161WnUTHsQ1JzdgmTZrJkckk0kSiYRrdZlZfFLMGiSGEouCcWHxJ0JIF4pkMkmxWAx0P5qFuv65ULJPsVh0b/5yfn+D3EqlQrFYZGBgoCk93uxCkUgkXOvP3C+XywXWSwVhtokyEctypqyodr34duLUOE2I1nrptK3IMmnisWhHapWecvACfnvfFur1Olt2ZRkaLbCwv/Xgw24zli8zNOrcUGJRbyJEEMsX9xoCNfMDDE333iHL+olGm28CkUgEdehC7nl4OwB6/Z55KVCt0sslniQ3c7P2yZyKK0Imbq18Pk88HndjSHKDzefznhomExGoSqXi1iyZAiUDCwG3754pULJWSQMX0fR3liiVSp46LXFN+uM+ZpGxn2Qy6bGQMpkMw8PDTe8r+4hrVJI3TCYq8J3JeVPtLKhLurYKy6ykJ53gsAP7eXyrk8B5r97BGSeunOFVjbPZyN5bvrh3wtT62ZbJZ6aXH3pga/ekKVCPbx2hUKx4kinmA0HiJK2D9u7d696s5aZrDhkUEYnH4/T29rrFrOl02u3FJ3GeSCTC2NiYpx8fjFs+5jgPSYYQATFv0v66KL+VZBbqml3IzdH1JrJG04KS8Rty3aZ1JO5Fs4hX4m9m8saePXsYGBhwRbNQKLixLFnnRG48Gbw4E1ZUu15813dzIZbZyTNWLXMF6pEnhjjp6AM9gxJnkrDxJ2H5Ab3uH7UMMJypHnelctUjkocExJ+Ehf1pli3qYfueHLVanXUbh2elu3VfaOXek8C97JPL5dwECemNJ2IyMDDguZmL9SFxHdO6KRaLnuQHuXnLOkqlEj09PW48KJ1Ou50bYDyrTsRHBEdqrsyUcEldl+QO6bQuiJtSrtEUaxHffD5Pb2+vR1TN9wXo6+tj7969ni7vIoYirpJoEovFWk4n9iOZfTPRoSJsq6Nvt9uutT5vepZjmW0sX9zLQYv72LJrjFq9zr16B887/uCZXhb1ej10/EmYTQMMN+8cc7tfLFmQ8QyfDGLVoYvcpsB6w+yMB+4Lrdx7Up8kMSe5uctoCbGgBgYGXMEQC6hYLLo99ETsisWie9M3RUrSzYVCoUBPTw+xWMwdLmgmEaRSKXdshtms1iy8Nd1tkgzRKtYjaxerTYTPPEcul/Nk1fkFKplMupaiuDxl3aYFJAIqMTw/QS7QUqkU2PkdaLIep5OwOYhZ308RWAGcBazryMoss4YTjhoPMT70+B5Pz7+ZYu9YidHGmPtkIhaYYBDEbHHzrd/a3D2iHU85ZIEbo9q2O+vG3uYDrabnylRcESdJgRbBkZt5b2+v21VBtpt1SP5JuJKmLUWy8Xi86WYtcS4RhKCWQqVSyRUF07IR68WM65RKJbcY10TWaiYiSI2VWH/mvmbcSTIJzfft6elxrUAzhd4UsnK5HDiyA3AF2Y853sT8jLLZLGNjY2Sz2Y4UB4eyoLTWbwp6XSn1TzithSzzmJXL+lmyMMPOoTyVao371+3ilGOXz+iazO4RBy3uDUwwCGI2DDCs173dy1eGsOIyqTiHLR/gb5uddH+9foiTnzqz/wfTRZB7TyyRcrns9qcT8RFrQFKw0+k0yWSyqcmrZLvJ0ELpsCBuQOlKIe47P2KdtRrbXiwWyWQyrlCICEjCgyke/um+gOsyFCvOTFnv6+vzNME12zzJcELAnfArpFIp8vm8x1Uorj753AqFgifBxLxeKfwN6icoVpSsyWxkGxTfmg72dXLbt3A6OVjmMZFIhBNWLXOf/+WxXRRKM9uQUrLxIJx7TwgaYNhthseKbmF1MhHjwJDFt2rleNbh2g1Ds6LSH5x2WLf/cT1f++GDU+oZGPTNW6yqUqnE6OioJ2VcXHSZTIYFCxaQSCQ87ZBg/OYvyQ1i5ZiFsGaKur8YNx6Pk06nGR4ebpvhls/nmywZsaZMS03aGPmvWyw8Qaw+aZMUNNTQzLqTGJlgJoeYiBVlNp71I+IEBMabzForfxxNtk83UxYopVQEZzDgyET7WuY+T14x6KaYl8pVHnxs94ytpTn+NLGLTJABhoA7wLDbmOnlK5b0hZ7we9jyAbfp7Ui2NGs6Yvzpwa2s2zhEvljhl3dvmJT7Mch1ZKacb9++nW3btrF792727nWsRxEdEadMJuOKk2k1AG5xq7jdUqmUu585cNDsdC4ut1gsNqFAwbgFaFokEkcSC1AE1nS/iQjJes0aLnCETq7Nj9kFwz8cUWZS+cfSSwxPnptkMpmmkSJB6e0Sq2uV1DLdhPrLUErtVErtMH9wYlEfAT497auyzDocK2o8FnX/up0zNsxw994C+aLzB5JJxTlgcHK1WTPd9sg/XiMssVjU02ler98zreuaChu3j3Lv2p3u82qtzh13b6RWC2fdBX3rloSC0dFRNmzYwOjoKCMjI27n8WQyyaJFi1zXndkwVc4nGXyxWMxTrCquLbEi0um0W38kFoSIhJwzTHdwc8w84LHW/C2VTDEyrS1xsQkSY2s1xl3iZn6BEiszqCuEiK0Z95MUdT9BVlSQJSjv2YlaqbAW1CXAB42fS3DaHB2htf7CtK/KMis5YuVC1/rIFys89PjMWFFm/GnFkr5JZxCZbr5ux6Eq1ZrHPdkuvTwIZbSyenTTzE48zhXK/OLPG5pcPVt3Z/lLI843EUGBd3GJ3X///QwNDbkznkZGRhgZGXEz9uQGLplr5g3ZFCj/jVNmOEmGnGkBDQwMuOIiomLWLZldK0wkZmN+FhKX8rdokniQKQDVarXpNXOCbtB7StKEvx2TnCvIBWwWA0s6fqv0cf/7mmnrfvx1ZdNF2F5818sPzsiMH2qtb9ZaPzbtK7LMWmLRCMcfOW5F3at3zEgMZ/Mk08v9zOQAwy07x6g0PrMF/SkG+yb3rXPZoh4W9DvHlMpVHt8yMz0S6/U6d96zkWwjozOTinPsk8dT3+96cCvDIeZu+QVKujOsXbuWHTt2uP3zJPVZbrCSXADjYmTGn+S1eDzu1kSZc6XkBm42fI3FYvT39zelWZtWVDqdDmzCKjEuU6T8Voxcr7gazS9WMutJrB85XmglUtIGyhQoER6zCa2/iFiuq9WcKkHESyww/5BI2acT7j2YRAxKKfXPSqktOB3NdyulnlBKhR5WaJkfHHX4Irff31i+jO7yIL1arc5mw+qZTPxJ8A8wDHMjnS7M7L2p1GBFIpGmOVEzwV8e2+UWcAO84KSVPOfpB7G4MUesUq1xxz0b24q/6ZKD8SLVLVu2sH79ejcVWqwqcWft2rXL41KTmiEz/iQ1UhKP8U/TjUajbpNVOa8kU5iuN8EsxDWz3UxEYMxUcvPGbabI+zEFJkigqtVq4HuCdySIfE4ieILfWhOra6IvZ3JNZmsm8/MIslCnk7AxqE8A7wauAJ4DPA/4AvBRpdQHO7Y6y6wjHoty3BFL3Of/98iO0PGG6WDHUI5SY8JvXybBYN/kq9v9Awy7GYdqN14jLEca2Xwbt4+R7XJn9l3DeX5//xb3+dOPWMKhyweIxaL/v70vjZLrLM98auuuvXpRS26tLcvWZ8nYGGwjvEAMxuyEHCCQBBJIYIZhmJAwYRgICYQJhDUcOFkOSSaTOSQncVhCICSTGDA22IB3LNuSP1uyJVlbL1J1rbeWrq75cev5+r23q1ut7qruUvt7ztGRuqrurfvVVX9Pve/7vM+Ll16zzcwnOzlVXFRMI6Mnuo2XSiUcPHgQ+XzeREySnAKBALLZrEmdLdSfIwcMMqKQ7uNUu1GIQAk2Zef+uo4c+QHMCRgkwuGwaa5ltCFrTJJIWJei3N2vIJT+gTx+scm8dNeQ6UQSIVWGvBZGcDK9uRj886aAORPcdj1TncRSI6h3AXi71vpLWuufaK3v1lp/AW4d6n3duzyLXsRzdg2jv8/dAKaLVRw+Mb1q7+1X7y23g30tBhjmSzWczbsqqnAouOBwxXMhnegzxzabTTz5zOpFsfWZWdx2z1E0ZudcMK4XPXEbB+N4nppLA//4kZPIFdtHqP6xFI7j4MCBA8b0lN/S+W2fvU6VSgX5fN7T+Cr94pjOo9ihWq2aCIqbNOtTsmGVjbt9fX2efh4eW6vVPPLtdk2trI05joNcLgfHcdBoNFAsFs2mXq1WPc2t7Ua7txNGkGj95Mn1c+0ESVleL6+RrzsXQTF6bfd75ifWbmCpBBUG8Eybxw/BnZJr8SxCXySEK0W94YHHJ1atjuMhqE3L2+CBtVHyPSPSe5tHzm1uuxikWOLxo6tHUHc/fMKQbCQUxMv37Zgnk3/B3k0YSrvf9Oszs/jBA8fb/v+QKblarYbDhw9jfHzcGLYCc+qwZDJpmlmr1SrGx8fNeHRGUexnYp0okUgYRwdu1tzcmforl8tGIVir1QwZySiKxNdsNuc13LJfSv7MiINKQa7dX6vxG7tKtEub8fNaaPxFuVyeR2xSoccx9bRB4jkX+t1dSITBtTBl2U0s9TfkcwD+XCm1jQ8opYYAfBrA57txYRa9jSsvHUGktTFNTTuewXvdwkxj1hPtLKf+RKzFAMNj0r1808o8AC/ZOmCuf2rawUT23FLoleLw8Wk8+tRcyu7Gq7ZgMD0/5RQKBXHztdvNJnp8ooADT3trZXJjrNVqOHXqFJ566ilDTlJoMDg4aOTWVNjlcjlMTEx4/Pl4Xqav/DOhZH+T9PmjQ7pMwTGFJ22SAJi+Jrkxs34FzMnmeQxVbzyXhByw6Ec7gpLTexeSgPvR19eHVCqFcDiMVCo1z8BWnrfd+fgaf42M52mn6OsklkpQbwFwLYCnlFJPKaWeAHASrhff+3z9URbPAsT6w7h817D5+f6D413/NnVqqmRSSwOpfiTPYbC6GDjAcO7c3U3zNWabeEZEf8utPxF9kRB2bp6bC3VXa25Xt1As13D7A3NJlF1bB7B359CCr980FMdVu+dqlXfvP2m8EwF4vsFns1k88sgjpk+H1j7BYBAjIyMYGxszdSKm6BzHQTabRaFQQLFY9KS2qN5jtOV3AKe1kUztMTKSFkDRaBTJpDdKZ7Tnr8kwMqGNEG2YSKj+RljK49s1AS+UxpPSdx7vf87fjyVrb37HCxJTO4JiBEgwimKUKl/Xzf93S9UGfrFrV2BxweKq3Rux/9AUZmebOH2mhJNTpWXXVZaC5bpHLITVHGA4fqZkxB3pRJ+Riq8E1+zZiMPHpzHbbOLkVBEHj5zF3p3D5z7wPDE728R37z2Gas29/lS8Dy+5eus56w/7Lr8IT5/MYbpQRa3ewA8eeAavu/FiM6IdAAqFAh566CEUCgUzDoPnTaVSUEohmUyiXq9jYsL9/kupdi6X89SLZD2IYgU6hBOBQADpdBr5fN6kqBbqcUokEsZ9XNaJGEVJ4YHsq6LbeqVSMcIGkgOvh9ZKPIbrZu2McnN/fUqKKui7ByycqiMh8b0o9uC5WNfz+/21G8PBaNI/HbiboziWahZrZkMppdIAglrr1auMW/QkkrEI9owN4bFW2ueBg+NdJajl+u8thNV0Npcp0O2bli/ukBjOxHDV7hE8qN2N++79JzE2mjZtAJ3Cg3rCfPaBQAC37NtuZPqLIRwK4uZrtuOf7jjkGuSeLuDxI1lcNjZoBAn79+/H5OSkISciFothz549GBgYwJYtW1Cv11EsFlEoFAzBlUolU4/h5kwyoBGsv87D1B2jLlkjkmIApgLZPCvdE0hYjuMYlwqKLeiDRwUfndal0MLfR0UByLzPT9SKCDntl9ERBy0S0odQOmhwsi8/LzkbiiM6/I7pEvF43Nw3vk+lUkG5XEYqlepKs+759EG9Ryn1DIAs3D6oU0qpD3X0aiwuODxfbTS/BMfGC2Zm0VLhVGdQKNeQL9WQK1aRLVRwNl/BmZyDqWkHk1kHE2fLODlZxETr3IFAAFs7QIQcYAjADDDsFo6Ny/EanZtBde3ei4y7R7XWwF1C/t0JnD5Twr2PnRbvtwmbN5yPOW8CV4rZVXc9fAL5oruBa61x5MgRz+bKyGHnzp3IZDKIxWKYmJjwOH1zY3UcB4VCAblcDqVSyUzeZXrMP+4dmIso+vv755mmSpdzEgbTbf7eJTYQU1wh60O0TwLmBBaAG3m1a/Kt1WqLRj8SfmFCf3//glJ7mSaUTbsyovSn+dqZwAIwpraycZcKRX4OSxl+eL5Y6sDCDwD4fQCfBHAXgACAGwB8SCnlaK2/1PErs7ggkEn249JtA8bF+sHHx/Gq63cu+HqmA58+lTfpn/PFhky0IyPP/QMMT58pdZQ8iHKljsms+600GAh0JPojIuEgbnr+Vnz7R08BcF3O1Y7BjgxirNYbuO2eo2aw4uYNCVwjXO2Xihc+ZxRHTuWRK1ZRbaX6LrtoFvv37/dIyZlm2rBhA4aHh5FIJMzGGY/HEY1GzWbOyKdSqRgvPsBNGaZSKZO686va6O7A56XTQjQanVfDAeYUf9K/jpEE043+Rlj6BPr9+ZhSk1goTdZuDDwAEwnJ1/jBVKN/7ayzSWLi59duJAjPxaiN0WmhUPBclxxr30ks9bf8vQD+i9b6H8RjdyuljgL4BABLUM9iXH3ZRkNQh0/kcDZfMTJjwLXkOTZewJGTORw5VVjxqI5OksjohoRxND851R2Cku4RoxsSHR8zv/2iNHZvHzT34M4Hj+OXX34ZIuHlp1uazSbueOC4GQvS3xfCLft2LHnulkQk7DbwfvOOQwCAQ8+cxVMHngQqJfNegEsSqVQKF198MUZGRua5a6fTaTiOA8dxzCbLepDjOEilUpidnUWhUDBkI9VwjL5yuRwKhYLZxOXYjXA4jHK5PG+AIBt4pXEsIwa/AKFerxtylQRIImhHOhx+6IecM0VIggLm3C38ykI/QUUiETiO4/lMSPZM9fmjNr9rhhxXItEtyflSCWoEwH1tHn8AwNrP/7ZYUwxnYti5OWN84R58fBz7njOKIyfzePpUDicmikZ950c4FES0L2Q2vmAgAATcv93CMRAQPw8k+/F84aq+UqzGAMNOuEecCzc+dzOOns6jWmsgX6rh/oOncd0Vm5d9vv2HpjwNwC+5ehtS8eUXwreMJHHFrg145NAkjh49CqdcxbZ4FeHArFHnRaNR7N27F6Ojo20bVTOZDKanpw05caw7XRrq9TpisZhxPee3fbpRAG6ExTSWNI1lapGEwPoSwb4fWWOSIgimE6XNUCgUMoQmFYjtNnJGUe0cLPygIIKkRHPYdhZLkvgoxPCn4tjrJI8jZF1J9kX5BRxS3NJJLJWgHgXwiwA+5Xv8LQAe7+gVWVyQuPqyjYagHj+aXbR5NBmLYGw0jbHNGWzdmFxRw+pK0W6A4VLnMy0Fs7NNL0GtsP9pIcSjEdxw5Wbcfr8rBX9IT2L39kEMZ87fiubhJybxo4dPmJ/37hzGJVsHFjliabj+ylF870cPoFgsAghgwklhJOIKbJLJJC6//HJs27ZtwfESsVjMNNByo+zv7zfCA8dxMDQ0hEAggEKhYExi5UYuZdE8nu7i7JviZi+dvuX4jUgk4klxkVgqlYrHm45ExcGExEKRht/0FWhPUNIMlyk5Xh+FHiQajuSQBOSf58Q+LX/EKZt8Zc2J7ycJ6lyms8vFUgnqowD+VSl1HYCftB67DsArAbyhGxdmcWHhouEEtm5MeUZhSIwMuFHW2GgaI4PtTS/XAhxgmC/VzADDi4Y7N7Z6ctoxKc14NIINA935RQaAPWNDePxIFieniphtNvGDB47jjS+55Lw+6wf1BH68f05osWkojhddtfxITCISDiHiHAXgfr7lRgwz4RDCzSa2bduGXbt2zUvr8Rs8N/XBwUGUy2UTMcmNlyTEOhOdvkk47epLkUjEEynRdYKpQ6bumMIql8ttjVPl4EMJ1q/8kKRBUKQhoxiSjb82RBEGPf0YudGTj68PBoMmZcm1VKtVTx2JZBMIBMznJ0d/+MmJnzdTlezx6sa4jaXKzG9TSt0M4DcB/CoAB8BBANdqrR/u+FVZXJDYd/ma0A55AAAgAElEQVRFODXlpvOCQVcMsHM0g7HN6RWlh7qNzRsSptZy54PH8crrxs57DEY75IpV3CGaW7dvOv/ZVeeDQCCAm67eilu/q40Y5dHDZ3CFUNEthvsPjuOnj54yP48OJ/C6F12MSLhzNbN0dBbpcBH5mSSCgSYCgSYikT5s3LgRjUbDWBVFo1HPt3daGsXjcUNcsn8JcFNfU1NTiMfjqNVqRpRAcmrnzpBKpRCPx1EqlUyKjMcyjccUHnuWpOqO95NRnCRFACZ16Xd5kD1QErVabV6azU9Q1WoV5XJ5Xk2Mn4G/HkWBB6XuUoLP50heHOLIz4qfQTuSZdTEGlg3oqglS6G01j8E8MOOX4HFusHohgTe/LLdKDp1jA53XgzQLVy6fdCkJCenHXz1+0/g5ft2rEgJd/RUHrfde9Q0twKA2rGw80KnMJSO4prLNuHeA640/CePnsLOLZlzum7ce+C0R06+eUMSr3vRzo6SEwBce+21yH//dsSDDsKBGcT7w9i8eTMmJyexbds2xOPxeWMluPkVi0XzzZ1j3+ncwAhC+tFRMMEajXQyD4VCGBgYwMDAgEmtsbGXJEPHiWw26xkV3y6iIXGxhsX3YCQindAJCjT8ruVyFD2vl5BTcdu5ULQjYTb20q2dkRbrSPQkpBuGn5wWskJKJBLms+/WF68FCUopFYfrIPEmAFUA3wTwIa11fqFjLCyGM7Fl1T3WEjsuSuOm52/FD392ArOzTVRrDXznrqex7/KLcPVlG8/rl6/ZbOK+g+O478Cc9VMwGMCLr9py3tNzl4vnX7YRTzyTNQ4OP/rZCbzqurEFr/eex07j/oPj5rGtG1N4zQ1jHScnALjhhhtQrVbx2GOPIRwOY2BgwPToHD16FLFYzJM2Ith02mg0kEgk4DiOiaLYh0N5OaMe6T7O85GootEoRkZGzHtzOi9JqF6vo1KpmDSWVKkxbUdS5CbNlGAwGESlUkEikTA1Jen6QFDA4a87sSZE8JySnID2BLWQR16z2TQSdzqr+6flOo6DRCJhCGcxcorFYh6Xim5hsaThxwG8DsBnAXwBwGsA/FXXrsTCYg3xnF0b8IabLjGRRrPZxE8fPYV/u/tpVJfYwFupzeBf734a9z522mxmyVgEb7jpEjxn19LSbJ1AOBTES642vs44fHy67eTdZrOJnzxyykNO2zel8NobOx85Sezbtw979uzBhg0bPPOSpqencfLkSdRqNZTLZc8GTDKQ5BUOhxGLxUwUUK/XTeMqveSkFx3TWdFoFAMDAyayYDqRogmm36anp825OR6DBrNy1pLswyIxSN88YP7oC4l2fU5+8vEPQlwIbCz22xGRxOX0YcrruQ4a6NKAdyFykunXbmMxgnoTgF/RWn9aa/05uCq+1yulOt+NZWHRA7ho2E1RSrump0/l8bXvPYEzufb2L8TUtIOvfu8JHBFTZrduTOLNL9vdUdHFUrFlJIk9Y3MpxR8+dAL1mbkNs9ls4u79J41NEuBGkq++YWfXVZWJRAKXXHLJvCipXq/j9OnTxvqoUqmYTZkKOkZB9LKLRqMmVVUqlUzNh1JoYK45l5s+m34JznViGoyWRPV63SPIKJVK5nHpOMF5U8DcCHYAJuoh2qXfKMjwQzqJy2v3g8TCCJCuF4lEwhAwa1bZbNak8mKxmKdJmjL62dlZFIvFBd+v3TDHbmKx/4lb4ZWQ39d6/fm3kvuglHqBdD5XSvUppf5SKXVWKTWplPqw7/VvVkodUkqVlFL/ptTcRDSl1Hal1HeVUgWl1GGl1KtXen0Wz17EoxG8/sW7PE7c08Uqvv79JxccDKiPnsXXb3/SCC0A1wLq51+0q+O+eOeDG67cjFjLcaNQruGeVo2p2Wzirp+dxM+emDSv3bk5g1dfP7Zqkv+NGzdi27ZtHgPSmZkZM3ojn8+bzZ7RFNNSkUjEzHqi310gEDBNvNJ6iLJwYG48eTs1X39/v6cOxTpTLpczEY2MwgAYWTrnSElLIwoHpCs4RRsSbPJt5y7Bfiv2dPlTaaFQyNTsYrEY0uk04vE44vE4UqmU+Vz6+/tNJFgqlUykGY1GjQsGPw824fqjOmA+OTGtSRLvRqPuYv8bQwDMVy6tdRNuLWrZciylVEAp9S4At/nO83EACsAuuGM93q6U+rXWMXsB/DWAdwAYBvAkgFvFsbcC2N967j8BuFUpdfFyr9HCIhgM4MbnbsErXrjDuDHUG7P4j58exV0PnzAj7huNWdz54HF8995jmGm0Nq1wEK+8bgzXX7l5Wa4LnUS0P4wbnzsnEX/4ySlMnC3jzodO4OFDc+S0a0sGr3zh/MGDXb22Vg1ocHDQjLjghl4qlTA+Pm5SUjMzM2aoIDA3MoO1GRq2kswYSXCjZYQQj8fNBsz+IIKOCYxc5Kwk2WxLCTfNZHkuqb5jipDnp18fz+EnGn9PEUGC5LW0k+BL+NNuvD4p2mg2mx7hSCaTmWfhxPeWIIHLa2NEyS8Sa+bF10F8HG4t6xMAfk88/nYA79BaZwFklVKfB/BuAF8B8DYA/6K1vgsAWtFVVil1KVxPwGsA3KK1rgG4XSn1bQDvBPCRVVqTxTrFpdsGMZSO4v/9+AimW2PLf/bEJCazDm587hbc+dBxnD4z5z4xmIri1dePtR3it1bY3VIoPjNeQLPZxDfvPIT6zNy340u2DuCWfTsQWmUyDQaDSKfT2Lp1K8rlstnMWbvJZrNIpVKmD4jmsNyUQ6EQUqmUIRr/SHZuyIxaEomEiUK4aUsbH0lmdKdgFFWpVEzNijUnOWKdvUUAjPIPcOtRTOtR6k4Jt38zp4BBkgyPYSqUjblMbUqi8/chSQNZHsN0ZKPRQKFQwMjIiHHfKJfL5r0pFCH6+/s9PVFSrCHHjJxrfPxycC6CeodSSk5yCwN4m1JqSr5Ia/3nS3y/L2utP6qUuokPKKUGAIwCOCBe9ziAK1r/3gvgfvFe5Zar+hUAZgEc01qXfMe+YInXY2GxKIYzMfziy3bje/ceM0KDE5NF/OP3tOd1u7YO4OZrtvWctD4QCODnnuf2Rs00Zj3ktHv7IF527fY1i/SYntqyZQuOHTtmZNcUOZw6dQr9/f2IxWJmQ6QSrq+vD0NDQzhx4oTH8JSzjGKxmEn/McIC5tSA7O1hYyvnNEnXbwAegiS5sM+IkRWfJ0mlUq5ak6lC2adVKpU8dTP5PtIwV6Ypq9WqUc3x3/4ozB89kYikuEQ6Z5CIKaogQTLVKudakZxI1iRgpjf5s2x47hQWI6hjAN7je+w0gF/3PdYEsCSC0lq3mwXAirTsOCsDiIvn/TMc5POLPWdhsWL0R0J49fVjeODxCdwjFHqAu7Fcd8Uonrd7pGfcMfwYSPXjmj2bPE24l+0YxEuvWTtyAmAIpNFoIJ/P48yZM6bGwxlM4+Pj2Lp1q9lsSQKxWMxDPJFIBJlMBrlczsjMk8mkiRJIcJSEUwAhU3VUuMlR8X7XcBJWs9lEsVg00VskEjERj0zJtSMURk/+Rl1eB2txxOzsLKrVKqLRqKkr+etEcsQH+5pIwmzM5RqSyaSJSGlnJO2RZOMuVZEyamJKz5+WXFUvPq31WMffrT0Y/cjmmTiAonje31jD5wOLPGdh0TEEAgFcs2cTRgZjuO0etwE31h/GK164oyPTfbuN5+0ewfGJIo5PFHDFrg140VVb1rxGBsC4OIyOjqJarZoeHZJFoVDAmTNnzHRbbp7FYhGpVAoDAwM4c+aMqWFlMhkEAgEkEgnTY9XX1+cZSij9+OjDx0iAjbXSkJXj2+nTR9ug/v5+IxBgjYZkwIhO/uwHxRSytkMy8oPnCofDRrhBUB5OOI6DarXqOQ8jpv7+ftN0zBlObNCVyj3WyhiRsqbGqIngZ9QuqusEVrsGNQ9a66xS6jRckQQdKi/DXMrvQOs5AKaBeHvr8QCA7UqpmNbaaXOshUVHseOiNN76istwcqqErSPJjsylWg2EQkG8/sUXoz4z21NpyHA4bOpKmzZtMpsfR0o0m01MT7vDu5l2klHB4OCgESkAc8V8pt0YHZCEpCQcgCcqA+ZsjqLRKBzHMdZAvFYSGocPUmXHSCkYDJroixEUz+PvT6LAQg5IlIax8rUUffin5/I5gmpHf6MuFXgkHBIVSUoKLtj3RUWjVFjKqK1Wq5nPq1KpYMOGzvf69cpv198C+JhSaj/clN4HMDdj6u8B3NWqW/0ErqP6Q1rrJwBAKfUwgE+2xBPXA3g9XCNbC4uuIB6NdMTde7URCAR6ipyIRCKBSqWCTCaDYrGIqakpNBoNIxqgOi8ajRr1GesqrDFJl20AJgXGDbTRaJh0lRzt7occJUEpO+tLrEEVCgVTn+HcKQo9SF58nmm7arVqyMc/ip1SbW7+7FNiSpHyeKbumM7jWHfpWcg+LZmGZpSWTCZNxCdJqlQqGaJk1JTP5zEzM2PcOfwj5Zk+lHUzzuTqJNZuzoEXH4U70uMxuP1W3wDwZQDQWj8C4DdaP08BuBxu0zDxRgB7AEwA+N8A3qm1fnTVrtzCwmJFYFotFAphZGQEyWRy3lA/x3FQLpdNrYRRR6PRMH1AsseIXnOlUgmlUslsvkzPtSMo6STBGgxdzTkll+RVLpeRz+fN+/D6pcO64ziejZ3RlTRf5XvQA5ARDo+VJqy8DrppxONxz5oprPA32ZIU+/r6kE6njTs7rZzi8bhJ3THK5HVMTEx4LJq4doouGG0ybdhprEkEpbW+A8CA+LkCd2rvexd4/Tfgkla7554B8KrOX6WFhcVqIZFIGA+64eFhs5ETrB0xemK6bHZ2FslkEqVSyWMHxH4qksacL2LQDB2kgk3Wn0hktVoNuVzORCdynAebf6X3HtN9MzMzpv4jIy+SDB0bODtKRmxSas9hiDL1R1KTfU1yLDwVkFLRx34p6a6eSqVMIzRrUNFo1DxGkibR5XI5pNNp87kQ7DcjIbers60UvZLis7CweBaD/m4zMzMYGBgwjgezs7OeAYCssfBvRkRS/MAoplQqmUZgPkYhQzqdNptxPB43kRB7qbjh+41k6ZhA8qjX64hGo4jFYnAcx5MSlFZFHBVCwQSFDZIoJZGyv4jpQ0YsBM1oqWRkis7v+UdbJ9aJAJi+MNaZWJejIzu/CBCNRgOTk5MmYmPEKBWIkUgE6XTnh3FagrKwsOgJJBIJ5HI5hEIhE0Xlcm7vGTdD6aYgx5EnEglMTk4aRRp7dvL5PEZGRkw9in1VjL7YqBqLxZDNZk06S1r/sA7GPitJABRZ8P1KpZKJdNiDxMimVquZ5mNu8GxwJUGQzEi8rE9FIhET2fDcAEzNyK+uA7ypPZ6fjc2s55H8SLycUMwUpyT+YrGIgYEBJBJeb8lkMolksjuzznqlBmVhYfEsRzwe9xBOJpNBKpUy9R+KEoD5zgnc+CnHpq2P4zjIZrMIBAImDSfTfX19fYZYWAuSNSBZGyKpSZBkeF6m06ShLaMhAGaKLSfUslmZ0Q/ThyRiXg+Nc3ntJEk+7/fCk/J4OQeLnzE/DzbokhjZg0bCZfqTUVOtVjMkFwqFMDQ0ZD77bsBGUBYWFj2DRCKBQqEAwB3vzvRTKpUyIgVuoHKWUigUQjKZRD7vuslToi5FDel02vj6sa+JqThJRoxE5EgKDkqUo9P96TjKuJnmY0qxXq+bFFw0GkU2mzXuDbxWXm88Hke5XDbHsw4kHRvoRDEzM2N89fzSc5Kk32GC9TJGiuzl4mcoU5hMe1JRyM/EcRxEIhGMjIx03dncEpSFhUXPgI277CMaGRnB2bNnjeTZ3/MjG11HRkZMDYnqN6bNCoUCSqWScWLgdF5GOExjyTHx7F1idEQSYKRGeyQSh5wlRY8+9nPV63UUi0VzPHuKeGwkEvFEkOVyGcVi0TMNl2Qqo0DWvUh4TOUxemoHRk88lq7mrD3xbzkQktfFz1UOYOwmSVmCsrCw6BkEg0HEYjGUSq7BTF9fHzZu3IhisWjk5/7Xs08IcCOwqakpMxuKtR8SzPT0tJGp02eOs6YIihYYEbGHSZrAknhIIACMaIPNx3w9iWlmZgbZbBbxeNzIskkAsiE4nU57RrMz0iLRklhJdBSTsEcslUp5UnuLgeIP9lnJdbF+VS6XjcKSMnV+LiTkbij4AEtQFhYWPQbKxglKo/3qMmDON45O5+FwGJs2bUKxWMT09DRmZmbMJssoiXUqEpOsK1FYwMiJaT/KqfmHdTG+FoCxWEqn0wgGg2ZAYDqdNrUnphsBeHqyKHLg+jj6g6a0jJJisZjxAGTtiNEV05GM3hYDSZZiDZIco0KKI0i2UthBUFIPwPSjdboWZQnKwsKip8AoSjaIsp7jb+AFYJR1tOxhzSoSiWBqaspEMIxoZJqsWCyalJVUBZIouOFKY1Q55p0beywWMxEZzzM8PGx6ntjnxXQfnSdY3wkGgyiVSoYwSXZMV1LuDrgkyjqV7Amj5J3nWmg0u3ReZ81N9lDJtB6jsmQyaaJRv4iDnxld5DsJS1AWFhY9h2Qy6SEoYI4s/CQlJdeMpFiLSSaTnm/6HCXBSIgO4UxdMWogQSUSCUMI/usAYKIVNgBHIhFUKhWjkNuwYQMmJyfNY47jGDJh+k5K4GmWK62c+B6Ug/P9GBGSvJn6YxREMYNfns4+Lyry2FNG/0FZV6Mgg8ew8Vk6vbczt+0ULEFZWFj0HDjqwb/5SYsebtQATPQgowNgbuZUsVg0I+HZtMvjZ2dncfbsWRMZ0U+Om3oymfTMZ2J0IWXvdKTgMUzX9ff3I5FImDQd02B8XzbixuNxk2Jj/YmRSiaTMb1W0naJpMX1xONxM/GXRC3Jh1FmpVIxKU8+L/36GC3y+ug2wUZmEqZ0nSBxdvz/QcfPaGFhYdEBJJPJtt/O2btEGTQH5VWrVc+MKAnWihhZULHH4whKrRlJyd4k6S4hPeiky7ccFEgCCYfDpleIaUjaFUlZO0FxB2tWqVQK0WjUNDFLHzzW0VgzYrqT856YcqPj+/T0tCFbNh7Tg5BrYMqRJEzrJkZpJEp695EYuwFLUBYWFj0Junr7U338tk+hgFTGLTR7iSPiaYyazWbnkR8Jy3Ec4zJOhRon2sp0Ius2JEyC4gUpKGCTMAAzl4n1GzpesMZGBR2PY6SyadMm5PN5E2VJ6yb+LaPLcDiMcrmMvr4+lEolM0dK9k1R/MDrpVyd10UhhTS05XvJ+lUmk/EoITsFS1AWFhY9i4GBAfT19RkVHEEJOOBusoVCAY7jIJlMesasE2xcpbtEOBzG1NSUIRam/LgJ0xWCERmjHJKBdASXhq8EJeFMrVH4Ie2EaNZKQpEqPlnjYcqRRJDP5z2eg6yTScskv2mt9DH0fy5cE2dpMVqUTcas2zGFKicTc3zH6OgoMplMJ2+/JSgLC4veBg1Pi8WiR37OyIXptmq1aiKj4eFhY3kk1XkATGPqyMgISqUSyuWyGchHoqLgQE6cBWBsj7ihUw7OzV/OegLmxn7wvLLWxDVQLEESkQ7pgKuO41ymer2OdDptakmM8BjVsFbVaDRQKpXmEZIE63xUH1IcIo1x5bBHNvbKZmhGko1GA7lcDps2berovbcEZWFh0fMIBoNIp9OIx+MoFAqGmKQ4gWkzx3EwMTGBTCaDWCzmUeAR3OxJYPV63eMEIV/HTV/2AVFBx1EanD1VqVRMhMXjuZmzdsRRG9JlXRq0Sj8+WgvNzMwgk8mY6CsejyOZTJoIkDW12dlZ5PN50/xL1wt/+i0cDiOTyZgIT47O4PpJvtJ7T7qqywGLVPd1GpagLCwsLhiEw2EMDg6iVqsZ3z1p5iobeamQk2k5IhAIIBaLGfcHyrOZ3uO/uQkzUpC1LyrcKC9PpVKYnp42aTUSBN0eGJlRDcfrYmqO9Sym10hsnAQ8PT2NRCLhGaqYSqVQLpcxPT2NYrFo3keOzSCxAHPzsAYGBkzkJImJdSc54VeSKyMt6a5BYup0eg+wBGVhYXEBgj1GjI7Y+CpBMQIAT1Orf+Q6wYiGtSCm5pjC4jmZOqTYgCQVCoUwMDCAbDZr0mxsdpVzmngdJBBZu2o2m0gkEsbtol6vI5/Pm3Qcff54fVNTU55x8rwuCia4FjpCcLRIMpk0BArAo8xjZClTkXSPp5giFot5SLSvr88SlIWFhYUE61PT09M4e/as5zmm16hMY12Fkmmq5iTYt0QlHkGzV3+diqKBer2ORCJhhiFyfAfJiZGdtFZimpDj6qX1EgUJJB1OCGYvVzKZNDJ0npc1MK6NNTpeUyQSMdGTbNylyMFxHLMWKcNnhMp/s67F1zAqsyo+CwsLCx+CwSCGhobQ19eHXC43bzYSN37pqMAUHcd3SKEBAGNlVC6XTQ1K+vORfDgdl9GHnO3ECIyRCKMf1nPYAMyNnj1GTPnJVCAJbGZmxqTypPs5U35yQm8ikcDIyIj5LPxRE30B6fTud1gH4HFyJ3HJSJD/LhQKCAQCGBwc7Oi9tQRlYWGxLkDVGwlBjlAH5kZF+CXowJwFEuXUPI8fjIRIOIzQKLSg6o/H+wckMlUniVAKHZhmJBmxB4kNv4ymZI8WHyeR8bzs3fJbMzWbTZRKJWSzWWOt5AfTmvwMKCbxD29kjY8ClU7DEpSFhcW6QDAYRCKRMH53crAhxQ+O45ieKAmmuqrVqsfrj6TGVB7rP8Bcio49TYxOWAPyizIoSGh33XJzZ/TG+hZrWSQSKv34M4UR8Xgcg4ODxnpICiXYtOs4DnK5nJHWtwOvnddPOTmfkxGhv1bVaViCsrCwWDegJx03aG607N9hv1Oj0TCmsgTJRgoDpPKOrg7SWZyvZdMtyYApN56HSrl2YA2KUR8wN2U3FouhVquZKcNStk5BBMmx2Wwin8/PI1DKxCnBl7Ot/CCJSkk9606sQ9ECifZKjN6sF5+FhYXFEsA0WqVSMTUeFvKZQiuXy57x6f7aFTd/El1fX5+xSuIkWooLmA6TEnOmxfygyIIbuyQCEghJhKq5VCpl+r+YTpMDEqX9EckLmBvoyPTjQlETlYV0dyepU4hBBaF0UJfTfuXQyE7CEpSFhcW6BNNeTJMBc6k2pv9oYSSl3n7IOhLJhyk4iiLk1F6mC2XDq4wy/MpBIhQKIRaLGRfxWq3mIU2SHZ3bZURGObx0XQdg3CZIIvL9+TcFFel0esEZUjw/Pw9pNkskEokFP8PlwhKUhYXFugUtg5jyk5DpP0Yk/tQX01mMSLgxA3NiDG7+VPoRJDY+L6MlKUKQf+R8J0ZPUkFHJSKFCiQyRjr8m/+mMzqvV/r19ff3Y2BgAENDQ0gmk/MIj9csRSWSnBjpMZKzBGVhYWGxDPhTfhLcrKWogmKIxWpHwWDQeOnJcRdyXEW9Xjezpkg+sq7VTlEIwDzPehPPRUskkg37tWSvEt+bj8v18pqj0ahxd2edio4YrKHxeL9EXp5LpjCr1aqdqGthYWGxHLRL+UkwcuFGLaOlhUCVHyMpNrqeCyQdOeKC6jwSAf9mtCKbZ9mzJU1sSVokJSmXp+KOUnTW3qhcJPgaijSYElyIpCXO9VktB5agLCwsnjWgTU87IqHvHdN0TLO166nyg4QhzWQXA1N8jFDK5bKnD0v+zUZipiBlDUsONvQPL5TCCZIZ3csXqoNJYQg/Ex4r1+jvGeNn22lYgrKwsHhWgVEPTVXZBCudJgh/+o8ptXZNvCS/cDhsnBnOx/5HRjo8HwBPXYrXQHKQTcCckcU0ItNtjGykNyBfxz8LkYvfGNdPTvw7Go1aLz4LCwuLTkE2o54LJAhGV1LkwA2fZBGJRJBOpz1zoqRvHuHf6PnHD2lWy+OkXx7TdlKlxxoSU368PnnN7AeTCkc/WVHoIZWM8nWMrOjEnkwmz+MOnBuWoCwsLCzOE4xSJGTaTkZC/udlFLbUCEtGO2zgbZdmk2B6UL6/rG/xWlnjoshDjoNvFzH5IWt1lqAsLCwsehBSoXcukMQo1ZYNsAA8xLBUkcJSrm8h1SAAT8RF+fhC6cx28Mv4O4F1QVBKqecC+DKAKwE8BeA3tNb3re1VWVhYWLSHVMslEgkT1bT7s1qQEVcsFvMILhglyev0R3+dlpgD64CglFJ9AL4F4IsAXgzgjQBuU0rt0Frn1/TiLCwsLJYA2RgrwdrPQsQlU39StNDuz/mSoD8i9DcBy0bkSCTS8fQesA4ICsBNACJa6y+2fr5VKfXfALwFwF+d49gQAJw+fbp7V2dhYWHR4/ATIYnHT3gAPP1Vst7mOM6y3lvsv/Pyj+uBoPYCOOh77HEAVyzh2FEAeOtb39rpa7KwsLCwOD+MAjgsH1gPBJUEUPY9VgYQX8Kx9wF4EYBTABZ2i7SwsLCw6BZCcMlpnm5gPRBUCYC/OhcHUDzXgVrrKoC7unFRFhYWFhZLxuF2D3bem2L1cQCA8j12WetxCwsLC4sLFOshgvoBgIBS6v0A/hSuiu9KAN9c06uysLCwsFgRLvgISmtdA/AquMR0FsBHAPyC1npyTS/MwsLCwmJFCJyPmaGFhYWFhcVq4YKPoCwsLCws1icsQVlYWFhY9CQsQVlYWFhY9CQsQVlYWFhY9CTWg8x83UEpdQuATwO4FMAEgM9prf+iZYz7pwDeBNf54gta60+J494M4I/gdmXfCeAdWuuJ1nPbAfw1gBe2zvmbWut/W71VzUeX1vlSAN8FII3BPqO1/sNVWNI8LHeN4vj3A/g5rfUviMd66l52aY09dR+BFf1//S0AvwVgGIAG8Dta6x+1nlsX9/Ica1z2vbQE1WNQSm0D8A0Ab4fr0n41gP9QSh2Ba4yrAOwCkAHw70qpE1rrryil9sL9j/4qAPcD+AyAWwG8tHXqWwH8BMBrANwI4J+VUldprZ9anZV50cV1Ph/A17TWv7Rqi1kAy11j69gkgI8B+B0A3/adumfuZYCbjZIAAAY3SURBVBfX2DP3EVjR/9c3APgggFvgeoS+HcB3lFKXtFphLvh7uYQ1LvteWoLqPYwB+HutNRuN71NK3QHgBrg3/h1a6yyArFLq8wDeDeArAN4G4F+01ncBgFLqw63XXAogAOAaALe0+sZuV0p9G8A74faNrQXG0OF1aq2fhPtL9bNVXcnCGMPy1ggA/wpgEsBfoGVqDABKqd3orXs5hg6vsYVeuo/A8tc5CuCPtNZ0tvkbpdQfA7hCKXUc6+NeLrhGALdjBffSElSPoRUW/4g/K6WG4Bra/i3c/wjSwkm6tu+FG1HwPGWl1DOt52cBHNNal3zHvqAba1gKurTOJ+F+WxtRSr0HLjH/I4Dfa/kuripWsEYA+GWt9Uml1B/Au3nvRQ/dyy6tEeih+wgsf51a6z+T51FKvRiuwfVjAK7DOriX51gjsIJ7aUUSPQylVAZu6uMeAA+0HpbO7dK1fTFX95U4vncdnVqnUioM4Dhcm6s9cNN+LwOwZnUL4jzXCK31yQVO1bP3slNr7OX7CJz/OsVxz8Hc5jyOdXQvxXGeNa70XtoIqkfRSuV8C+63lrdizrFdOrdL1/bFXN0Dizy3pujkOrXWMwBuFo8fUkp9Em6d6oMdvvQlYxlrXAzLdu/vJjq5xl69j8Dy16mUei3cSOTTWuvPth5eV/ey3RpXei9tBNWDaIXI9wD4ZwBv0lpXWrnf0/A6t0vXdo+ru1IqDmB76/EDALYrpWILHLsm6PQ6lVJblFKfbymOiD4AlS4uY1Esc42LoefuZafX2Iv3EVj+OlsKt38A8C6t9WfE69bNvVxojSu9lzaC6jEopXYB+A6Aj2it/8T39N8C+JhSaj/c9MAHAHyp9dzfA7hLKXUTXFXQpwA8pLV+onXehwF8siUquB7A6+HmwNcE3VinUioK9xtfWSn1vwDsBPB7AP5Pt9fTDitY44LQWuteupfdWCOAM+ih+wgsf51qriXipVrre+RB6+VeLrZGrPBeWoLqPbwXQArAp5RSsmfkzwB8FMAfwy0+BgH8JYAvA4DW+hGl1G+0ft4C91vQL4rj39h6/QSAKQDv1Fo/2t2lLIqOr1NrXVFKvQrAF+H+YpRbx35hNRbUBsta4xLQS/ey42vswfsILH+dHwLQD+D7SnnG1v2S1vo7WB/3ctE1ruReWjdzCwsLC4uehK1BWVhYWFj0JCxBWVhYWFj0JCxBWVhYWFj0JCxBWVhYWFj0JCxBWVhYWFj0JCxBWVhYWFj0JCxBWVj0AJRStyqlJlsGnf7nPqKUKiqlxtbg0iws1gyWoCwsegO/Dbdx3jPQTym1E+7ohY9orY+swXVZWKwZLEFZWPQAtNanAXwYwLuUUvvEU38Cd5aO33rGwmLdwzpJWFj0CJRSAQB3wzXTvBbAzwP4KoDncRicUurXAfwuXJung3Ajq39vPReGO8bgVwBshmst8w8APqC1biil/g7ubLA9cCejvkFrfceqLdDC4jxhIygLix6B1roJ4D8DuBLArwP4PIBPCHJ6DVwPs4+0XvPXAL6plOKAuw8D+GUAvwrg0tbP7wPwWvE2b4Nr8nkzgJ92eUkWFiuCNYu1sOghaK0fVUp9Ae4Y9IMAPi2e/l24s3a+2vr5kFLqGgD/HcAvAXgE7ljuH7ae/xul1AfhTuH9VuuxA1rrv+v2OiwsOgFLUBYWvYePA/ifAP5Qa10Xj+8FcLVS6vfFYxG05vJorf9ZKXWzUupzAHbDjbLGAITE6w9388ItLDoJm+KzsOgxaK2d1j8d31NhAP8DwFXiz+VwZwhBKfUJAF+D+3v9NQAvB+Af3eA/p4VFz8JGUBYWFw4eB7BDa32ID7SiqQqAzwH4rwB+W2v9ldZzMbjThgNrcK0WFiuGJSgLiwsHnwXwFaWUBnA7gFsAfAxu/QkAzgJ4rVLqxwAycFOFabjD5CwsLjjYFJ+FxQUCrfXXALwfwAfh1p3eD+DdWuuvt17ya3DVe48A+CcAhwD8XwBXr/rFWlh0ALYPysLCwsKiJ2EjKAsLCwuLnoQlKAsLCwuLnoQlKAsLCwuLnoQlKAsLCwuLnoQlKAsLCwuLnoQlKAsLCwuLnoQlKAsLCwuLnoQlKAsLCwuLnsT/B13eWp7i+InPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "plot_many_simulations(system, update_func2, 30)\n",
    "plot_population(pop_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `run_many_simulations` to collect the results and `describe` to summarize the distribution of net changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     1000.000000\n",
       "mean       742.390577\n",
       "std       3254.531642\n",
       "min      -1167.871448\n",
       "25%       -828.100752\n",
       "50%       -291.246535\n",
       "75%        958.199207\n",
       "max      35927.186006\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "last_pops = run_many_simulations(system, update_func2, 1000)\n",
    "net_changes = last_pops - p_0\n",
    "net_changes.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does the refined model have much effect on the probability of population decline?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.582"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "np.mean(net_changes < 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting data from a PDF document\n",
    "\n",
    "The following section uses `tabula-py` to get data from a PDF document.\n",
    "\n",
    "If you don't already have it installed, and you are using Anaconda, you can install it by running the following command in a Terminal or Git Bash:\n",
    "\n",
    "```\n",
    "conda install -c conda-forge tabula-py\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tabula import read_pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>1 SW</th>\n",
       "      <th>2SW</th>\n",
       "      <th>3SW</th>\n",
       "      <th>Repeat</th>\n",
       "      <th>Total</th>\n",
       "      <th>Hatchery</th>\n",
       "      <th>Natural</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997</td>\n",
       "      <td>278</td>\n",
       "      <td>1,492</td>\n",
       "      <td>8</td>\n",
       "      <td>36</td>\n",
       "      <td>1,814</td>\n",
       "      <td>1,296</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>340</td>\n",
       "      <td>1,477</td>\n",
       "      <td>3</td>\n",
       "      <td>42</td>\n",
       "      <td>1,862</td>\n",
       "      <td>1,146</td>\n",
       "      <td>716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999</td>\n",
       "      <td>402</td>\n",
       "      <td>1,136</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>1,567</td>\n",
       "      <td>959</td>\n",
       "      <td>608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>292</td>\n",
       "      <td>535</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>847</td>\n",
       "      <td>562</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001</td>\n",
       "      <td>269</td>\n",
       "      <td>804</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1,084</td>\n",
       "      <td>833</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2002</td>\n",
       "      <td>437</td>\n",
       "      <td>505</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>967</td>\n",
       "      <td>832</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2003</td>\n",
       "      <td>233</td>\n",
       "      <td>1,185</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1,427</td>\n",
       "      <td>1,238</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2004</td>\n",
       "      <td>319</td>\n",
       "      <td>1,266</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>1,630</td>\n",
       "      <td>1,395</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2005</td>\n",
       "      <td>317</td>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1,272</td>\n",
       "      <td>1,019</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2006</td>\n",
       "      <td>442</td>\n",
       "      <td>1,007</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1,456</td>\n",
       "      <td>1,167</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2007</td>\n",
       "      <td>299</td>\n",
       "      <td>958</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1,261</td>\n",
       "      <td>940</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2008</td>\n",
       "      <td>812</td>\n",
       "      <td>1,758</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>2,605</td>\n",
       "      <td>2,191</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2009</td>\n",
       "      <td>243</td>\n",
       "      <td>2,065</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2,340</td>\n",
       "      <td>2,017</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2010</td>\n",
       "      <td>552</td>\n",
       "      <td>1,081</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>1,651</td>\n",
       "      <td>1,468</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2011</td>\n",
       "      <td>1,084</td>\n",
       "      <td>3,053</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>4,178</td>\n",
       "      <td>3,560</td>\n",
       "      <td>618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012</td>\n",
       "      <td>26</td>\n",
       "      <td>879</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>941</td>\n",
       "      <td>731</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>78</td>\n",
       "      <td>525</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>611</td>\n",
       "      <td>413</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014</td>\n",
       "      <td>110</td>\n",
       "      <td>334</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>450</td>\n",
       "      <td>304</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2015</td>\n",
       "      <td>150</td>\n",
       "      <td>761</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>921</td>\n",
       "      <td>739</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016</td>\n",
       "      <td>232</td>\n",
       "      <td>389</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>626</td>\n",
       "      <td>448</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2017</td>\n",
       "      <td>363</td>\n",
       "      <td>663</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>1041</td>\n",
       "      <td>806</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0   1 SW    2SW  3SW  Repeat  Total Hatchery  Natural\n",
       "0         1997    278  1,492    8      36  1,814    1,296      518\n",
       "1         1998    340  1,477    3      42  1,862    1,146      716\n",
       "2         1999    402  1,136    3      26  1,567      959      608\n",
       "3         2000    292    535    0      20    847      562      285\n",
       "4         2001    269    804    7       4  1,084      833      251\n",
       "5         2002    437    505    2      23    967      832      135\n",
       "6         2003    233  1,185    3       6  1,427    1,238      189\n",
       "7         2004    319  1,266   21      24  1,630    1,395      235\n",
       "8         2005    317    945    0      10  1,272    1,019      253\n",
       "9         2006    442  1,007    2       5  1,456    1,167      289\n",
       "10        2007    299    958    3       1  1,261      940      321\n",
       "11        2008    812  1,758   12      23  2,605    2,191      414\n",
       "12        2009    243  2,065   16      16  2,340    2,017      323\n",
       "13        2010    552  1,081    2      16  1,651    1,468      183\n",
       "14        2011  1,084  3,053   26      15  4,178    3,560      618\n",
       "15        2012     26    879   31       5    941      731      210\n",
       "16        2013     78    525    3       5    611      413      198\n",
       "17        2014    110    334    3       3    450      304      146\n",
       "18        2015    150    761    9       1    921      739      182\n",
       "19        2016    232    389    2       3    626      448      178\n",
       "20        2017    363    663   13       2   1041      806      235"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = read_pdf('data/USASAC2018-Report-30-2017-Activities-Page11.pdf')"
   ]
  }
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